Patent Publication Number: US-8994822-B2

Title: Infrastructure mapping system and method

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation-in-part of U.S. patent application Ser. No. 12/798,899, filed on Apr. 13, 2010, which claimed priority to prior Ser. No. 11/581,235 (now issued as U.S. Pat. No. 7,725,258), filed on Oct. 11, 2006, which claimed priority to prior U.S. patent application Ser. No. 10/664,737 (now issued as U.S. Pat. No. 7,127,348), filed on Sep. 18, 2003, which claimed priority to U.S. Provisional Patent Application Ser. No. 60/412,504, filed on Sep. 20, 2002 for “Vehicle Based Data Collection and Processing System.” This application is also a continuation-in-part of U.S. application Ser. No. 12/462,533, filed on Aug. 5, 2009, which is a division of U.S. patent application Ser. No. 10/229,626 (now issued as U.S. Pat. No. 7,893,957), filed on Aug. 28, 2002. 
    
    
     TECHNICAL FIELD OF THE INVENTION 
     The present invention relates, generally, to the field of remote imaging techniques and, more particularly, to a system for rendering high-resolution, high accuracy, low distortion digital images over very large fields of view. 
     BACKGROUND OF THE INVENTION 
     Remote sensing and imaging are broad-based technologies having a number of diverse and extremely important practical applications—such as geological mapping and analysis, and meteorological forecasting. Aerial and satellite-based photography and imaging are especially useful remote imaging techniques that have, over recent years, become heavily reliant on the collection and processing of data for digital images, including spectral, spatial, elevation, and vehicle or platform location and orientation parameters. Spatial data—characterizing real estate improvements and locations, roads and highways, environmental hazards and conditions, utilities infrastructures (e.g., phone lines, pipelines), and geophysical features—can now be collected, processed, and communicated in a digital format to conveniently provide highly accurate mapping and surveillance data for various applications (e.g., dynamic GPS mapping). Elevation data may be used to improve the overall system&#39;s spatial and positional accuracy and may be acquired from either existing Digital Elevation Model (DEM) data sets or collected with the spectral sensor data from an active, radiation measuring Doppler based devices, or passive, stereographic calculations. 
     Major challenges facing remote sensing and imaging applications are spatial resolution and spectral fidelity. Photographic issues, such as spherical aberrations, astigmatism, field curvature, distortion, and chromatic aberrations are well-known problems that must be dealt with in any sensor/imaging application. Certain applications require very high image resolution—often with tolerances of inches. Depending upon the particular system used (e.g., automobile, aircraft, satellite, space vehicle or platform), an actual digital imaging device may be located anywhere from several feet to miles from its target, resulting in a very large scale factor. Providing images with very large scale factors, that also have resolution tolerances of inches, poses a challenge to even the most robust imaging system. Thus, conventional systems usually must make some trade-off between resolution quality and the size of a target area that can be imaged. If the system is designed to provide high-resolution digital images, then the field of view (FOV) of the imaging device is typically small. If the system provides a larger FOV, then usually the resolution of the spectral and spatial data is decreased and distortions are increased. 
     Ortho-imaging is an approach that has been used in an attempt to address this problem. In general, ortho-imaging renders a composite image of a target by compiling varying sub-images of the target. Typically, in aerial imaging applications, a digital imaging device that has a finite range and resolution records images of fixed subsections of a target area sequentially. Those images are then aligned according to some sequence to render a composite of a target area. 
     Often, such rendering processes are very time-consuming and labor intensive. In many cases, those processes require iterative processing that measurably degrades image quality and resolution—especially in cases where thousands of sub-images are being rendered. In cases where the imaging data can be processed automatically, that data is often repetitively transformed and sampled—reducing color fidelity and image sharpness with each successive manipulation. If automated correction or balancing systems are employed, such systems may be susceptible to image anomalies (e.g., unusually bright or dark objects)—leading to over or under-corrections and unreliable interpretations of image data. In cases where manual rendering of images is required or desired, time and labor costs are immense. 
     There is, therefore, a need for a metric camera system that provides metric accuracy, stability and repeatable imaging. In particular, there is a need for an ortho-image and an optional oblique-image rendering system that provides efficient and versatile imaging for very large FOVs including infrastructure and associated data sets, while maintaining such metric image quality, accuracy, positional accuracy and clarity. Additionally, automation algorithms are applied extensively in every phase of the planning, collecting, navigating, and processing all related operations. 
     SUMMARY OF THE INVENTION 
     The present invention relates to remote data collection and processing system using a variety of sensors. The system may include computer console units that control vehicle and system operations in real-time. The system may also include global positioning systems that are linked to and communicate with the computer consoles. Additionally, cameras and/or camera array assemblies can be employed for producing an image of a target viewed through an aperture. The camera array assemblies are communicatively connected to the computer consoles. The camera array assembly has a mount housing, a first imaging sensor centrally coupled to the housing having a first focal axis passing through the aperture. The camera array assembly also has a second imaging sensor coupled to the housing and offset from the first imaging sensor along an axis, that has a second focal axis passing through the aperture and intersecting the first focal axis within an intersection area. The camera array assembly has a third imaging sensor, coupled to the housing and offset from the first imaging sensor along the axis, opposite the second imaging sensor, that has a third focal axis passing through the aperture and intersecting the first focal axis within the intersection area. Any number of one-to-n cameras may be used in this manner, where “n” can be any odd or even number. 
     The system may also include an Attitude Measurement Unit (AMU) such as inertial, optical, or similar measurement units communicatively connected to the computer consoles and the camera array assemblies. The AMU may determine the yaw, pitch, and/or roll of the aircraft at any instant in time and successive DGPS positions may be used to measure the vehicle heading with relation to geodesic north. The AMU data is integrated with the precision DGPS data to produce a robust, real-time AMU system. The system may further include a mosaicing module housed within the computer consoles. The mosaicing module includes a first component for performing initial processing on an input image. The mosaicing module also includes a second component for determining geographical boundaries of an input image with the second component being cooperatively engaged with the first component. The mosaicing module further includes a third component for mapping an input image into the composite image with accurate geographical position. The third component being cooperatively engaged with the first and second components. A fourth component is also included in the mosaicing module for balancing color of the input images mapped into the composite image. The fourth component can be cooperatively engaged with the first, second and third components. Additionally, the mosaicing module can include a fifth component for blending borders between adjacent input images mapped into the composite image. The fifth component being cooperatively engaged with the first, second, third and fourth components. 
     A sixth component, an optional forward oblique and/or optional rear oblique camera array system may be implemented that collects oblique image data and merges the image data with attitude and positional measurements in order to create a three dimensional image (i.e., 3D point cloud) or digital elevation model (DEM). The 3D point cloud or DEM is a representation of the ground surface including man made structures. The DEM may be created using stereographic techniques from ortho and/or oblique imagery, or, alternatively, provided by LIDAR or an existing DEM. The DEM or 3D point cloud may be created from any overlapping images from a single camera overlapping in time or overlapping images from any two cameras in overlapping space and/or time. Creation of which may be performed in real-time onboard the vehicle or post processed later. This sixth component works cooperatively with the other components. All components may be mounted to a rigid platform mount for the purpose of providing co-registration of sensor data. Vibrations, turbulence, temperature gradients and other forces may act on the vehicle or platform in such a way as to create errors in the alignment relationship between sensors. Utilization of common, rigid platform mount and/or thermal sleeves for the sensors provides a significant advantage over other systems that do not use this co-registration architecture. 
     Further, the present invention may employ a certain degree of lateral oversampling to improve output quality and/or co-mounted, co-registered oversampling to overcome physical pixel resolution limits. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a better understanding of the invention, and to show by way of example how the same may be carried into effect, reference is now made to the detailed description of the invention along with the accompanying figures in which corresponding numerals in the different figures refer to corresponding parts and in which: 
         FIG. 1  illustrates a vehicle based data collection and processing system of the present invention; 
         FIG. 1A  illustrates a portion of the vehicle based data collection and processing system of  FIG. 1 ; 
         FIG. 1B  illustrates a portion of the vehicle based data collection and processing system of  FIG. 1 ; 
         FIG. 2  illustrates a vehicle based data collection and processing system of  FIG. 1  with the camera array assembly of the present invention shown in more detail; 
         FIG. 3A  illustrates camera array assembly configured in across track, cross-eyed fashion in accordance with certain aspects of the present invention; 
         FIG. 3B  illustrates a camera array assembly configured in a long track, cross-eyed fashion in accordance with certain aspects of the present invention; 
         FIG. 3C-1  illustrates a camera array assembly configured in along track, cross-eyed fashion in accordance with certain aspects of the present invention; 
         FIG. 3C-2  illustrates a sequence of images obtained from a camera array assembly configured in along track, cross-eyed fashion in accordance with certain aspects of the present invention; 
         FIG. 3D  illustrates a camera array assembly configured in across track, cross-eyed and along track, cross-eyed fashion in accordance with certain aspects of the present invention; 
         FIG. 3E  illustrates a camera array assembly configured in a long track, cross-eyed and across track, cross-eyed fashion in accordance with certain aspects of the present invention; 
         FIG. 4A  illustrates one embodiment of an imaging pattern retrieved by the camera array assembly of  FIGS. 1 and 3A ; 
         FIG. 4B  illustrates one embodiment of an imaging pattern retrieved by the camera system of  FIGS. 1 and 3B ; 
         FIG. 4C-1  illustrates one embodiment of an imaging pattern retrieved by the camera system of FIGS.  1  and  3 C- 1 ; 
         FIG. 4C-2  illustrates one embodiment of an imaging pattern retrieved by the camera system of FIGS.  1  and  3 C- 2 ; 
         FIG. 4D  illustrates one embodiment of an imaging pattern retrieved by the camera system of  FIGS. 1 and 3D ; 
         FIG. 4E  illustrates one embodiment of an imaging pattern retrieved by the camera system of  FIGS. 1 and 3E ; 
         FIG. 5  depicts an imaging pattern illustrating certain aspects of the present invention; 
         FIG. 6  illustrates an image strip in accordance with the present invention; 
         FIG. 7  illustrates another embodiment of an image strip in accordance with the present invention; 
         FIG. 8  illustrates one embodiment of an imaging process in accordance with the present invention; 
         FIG. 9  illustrates diagrammatically how photos taken with the camera array assembly can be aligned to make an individual frame; 
         FIG. 10  is a block diagram of the processing logic according to certain embodiments of the present invention; 
         FIG. 11  illustrates lateral oversampling looking down from a vehicle according to certain embodiments of the present invention; 
         FIG. 12  illustrates lateral oversampling looking down from a vehicle according to certain embodiments of the present invention; 
         FIG. 13  illustrates flight line oversampling looking down from a vehicle according to certain embodiments of the present invention; 
         FIG. 14  illustrates flight line oversampling looking down from a vehicle according to certain embodiments of the present invention; 
         FIG. 15  illustrates progressive magnification looking down from a vehicle according to certain embodiments of the present invention; 
         FIG. 16  illustrates progressive magnification looking down from a vehicle according to certain embodiments of the present invention; 
         FIG. 17  illustrates progressive magnification looking down from a vehicle according to certain embodiments of the present invention; 
         FIG. 18  is a schematic of the system architecture according to certain embodiments of the present invention; 
         FIG. 19  illustrates lateral co-mounted, co-registered oversampling in a sidelap sub-pixel area for a single camera array looking down from a vehicle according to certain embodiments of the present invention; 
         FIG. 20  illustrates lateral co-mounted, co-registered oversampling in a sidelap sub-pixel area for two overlapping camera arrays looking down from a vehicle according to certain embodiments of the present invention; 
         FIG. 21  illustrates fore and lateral co-mounted, co-registered oversampling in sidelap sub-pixel areas for two stereo camera arrays looking down from a vehicle according to certain embodiments of the present invention; 
         FIG. 22A  illustrates a rear right side perspective view of the camera array of  FIG. 3D ; 
         FIG. 22B  illustrates a front right side perspective view of the camera array of  FIG. 3E ; 
         FIG. 23  illustrates a bottom view of concave or retinal camera array assembly configured in across track, cross-eyed and long track, cross-eyed fashion in accordance with certain aspects of the present invention; 
         FIG. 24  illustrates one embodiment of an oblique camera array assembly in accordance with certain aspects of the present invention; 
         FIG. 25  illustrates an image strip in accordance with the present invention; 
         FIG. 26A  illustrates one embodiment of a camera array assembly configured in along track, cross-eyed fashion in accordance with certain aspects of the present invention; and 
         FIG. 26B  illustrates a bottom view of the camera array of  FIG. 26A . 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     While the making and using of various embodiments of the present invention are discussed in detail below, it should be appreciated that the present invention provides many applicable inventive concepts, which can be embodied in a wide variety of specific contexts. The specific embodiments discussed herein are merely illustrative of specific ways to make and use the invention and do not limit the scope of the invention. 
     A vehicle based data collection and processing system  100  of the present invention is shown in  FIGS. 1 ,  1 A &amp;  1 B. Additional aspects and embodiments of the present invention are shown in  FIGS. 2 &amp; 18 . System  100  includes one or more computer consoles  102 . The computer consoles contain one or more computers  104  for controlling both vehicle and system operations. Examples of the functions of the computer console are the controlling digital color sensor systems that can be associated with the data collection and processing system, providing the display data to a pilot, coordinating the satellite generated GPS pulse-per-second (PPS) event trigger (which may be 20 or more pulses per second), data logging, sensor control and adjustment, checking and alarming for error events, recording and indexing photos, storing and processing data, flight planning capability that automates the navigation of the vehicle, data, and providing a real-time display of pertinent information. A communications interface between the control computer console and the vehicle autopilot control provides the ability to actually control the flight path of the vehicle in real-time. This results in a more precise control of the vehicle&#39;s path than is possible by a human being. All of these functions can be accomplished by the use of various computer programs that are synchronized to the GPS PPS signals and take into account the various electrical latencies of the measurement devices. In an embodiment, the computer is embedded within the sensor. 
     One or more differential global positioning systems  106  are incorporated into the system  100 . The global positioning systems  106  are used to navigate and determine precise flight paths during vehicle and system operations. To accomplish this, the global positioning systems  106  are communicatively linked to the computer console  102  such that the information from the global positioning systems  106  can be acquired and processed without flight interruption. Zero or more GPS units may be located at known survey points in order to provide a record of each sub-seconds&#39; GPS satellite-based errors in order to be able to back correct the accuracy of the system  100 . GPS and/or ground based positioning services may be used that eliminate the need for ground control points altogether. This technique results in greatly improved, sub-second by sub-second positional accuracy of the data capture vehicle. 
     One or more AMUs  108  that provide real-time yaw, pitch, and roll information that is used to accurately determine the attitude of the vehicle at the instant of data capture are also communicatively linked to the computer console  102 . The present attitude measurement unit (AMU) (e.g., Applanix POS AV), uses three high performance fiber optic gyros, one gyro each for yaw, pitch, and roll measurement. AMUs from other manufacturers, and AMUs that use other inertial measurement devices can be used as well. Additionally, an AMU may be employed to determine the instantaneous attitude of the vehicle and make the system more fault tolerant to statistical errors in AMU readings. Connected to the AMU can be one or more multi-frequency DGPS receivers  110 . The multi-frequency DGPS receivers  110  can be integrated with the AMU&#39;s yaw, pitch, and roll attitude data in order to more accurately determine the location of the remote sensor platform in three dimensional space. Additionally, the direction of geodesic North may be determined by the vector created by successive DGPS positions, recorded in a synchronized manner with the GPS PPS signals. 
     One or more camera array assemblies  112  for producing an image of a target viewed through an aperture are also communicatively connected to the one or more computer consoles  102 . The camera array assemblies  112 , which will be described in greater detail below, provide the data collection and processing system with the ability to capture high resolution, high precision progressive scan or line scan, color digital photography. 
     The system may also include DC power and conditioning equipment  114  to condition DC power and to invert DC power to AC power in order to provide electrical power for the system. The system may further include a navigational display  116 , which graphically renders the position of the vehicle versus the flight plan for use by the pilot (either onboard or remote) of the vehicle to enable precision flight paths in horizontal and vertical planes. The system may also include an EMU module comprised of LIDAR, SAR  118  or a forward and rear oblique camera array for capturing three dimensional elevation/relief data. The EMU module  118  can include a laser unit  120 , an EMU control unit  122 , and an EMU control computer  124 . Temperature controlling devices, such as solid state cooling modules, can also be deployed as needed in order to provide the proper thermal environment for the system. 
     The system also includes a mosaicing module, not depicted, housed with the computer console  102 . The mosaicing module, which will be described in further detail below, provides the system the ability to gather data acquired by the global positioning system  106 , the AMU  108 , and the camera system  112  and process that data into useable orthomaps. 
     The system  100  also can include a self-locking flight path technique that provides the ability to micro-correct the positional accuracy of adjacent flight paths in order to realize precision that exceeds the native precision of the AMU and DGPS sensors alone. 
     A complete flight planning methodology is used to micro plan all aspects of missions. The inputs are the various mission parameters (latitude/longitude, resolution, color, accuracy, etc.) and the outputs are detailed on-line digital maps and data files that are stored onboard the data collection vehicle and used for real-time navigation and alarms. The ability to interface the flight planning data directly into the autopilot is an additional integrated capability. A computer program may be used that automatically controls the flight path, attitude adjustments, graphical display, moving maps of the vehicle path, checks for alarm conditions and corrective actions, notifies the pilot and/or crew of overall system status, and provides for fail-safe operations and controls. Safe operations parameters may be constantly monitored and reported. Whereas the current system uses a manned crew, the system is designed to perform equally well in an unmanned vehicle. 
       FIG. 2  shows another depiction of the present invention. In  FIG. 2 , the camera array assembly  112  is shown in more detail. As is shown, the camera array assembly  112  allows for images to be acquired from the rear oblique, the forward oblique and the nadir positions. 
       FIGS. 3A-3E  describe in more detail examples of camera array assemblies of the present invention. In particular,  FIGS. 3A-3E  provide examples of camera array assemblies  300  airborne over target  302  (e.g., terrain). For illustrative purposes, the relative size of assembly  300 , and the relative distance between it and terrain  302 , are not depicted to scale in  FIGS. 3A-3E . The camera array assembly  300  comprises a housing  304  within which imaging sensors  306 ,  308 ,  310 ,  312  and  314  are disposed along a concave curvilinear axis  316 . In a preferred embodiment, the housing may be a mount unit. Assembly  300  is adapatably mountable to a vehicle that moves with respect to a terrain along a path. The radius of curvature of axis  316  may vary or be altered dramatically, providing the ability to effect very subtle or very drastic degrees of concavity in axis  316 . Alternatively, axis  316  may be completely linear—having no curvature at all. The imaging sensors  306 ,  308 ,  310 ,  312  and  314  couple to the housing  304 , either directly or indirectly, by attachment members  318 . Attachment members  318  may comprise a number of fixed or dynamic, permanent or temporary, connective apparatus. For example, the attachment members  318  may comprise simple welds, removable clamping devices, or electro-mechanically controlled universal joints. 
     Additionally, the system  100  may have a real-time, onboard navigation system to provide a visual, bio-feedback display to the vehicle pilot, or remote display in the case of operations in an unmanned vehicle. The pilot is able to adjust the position of the vehicle in real-time in order to provide a more accurate flight path. The pilot may be onboard the vehicle or remotely located and using the flight display to control the vehicle through a communication link. 
     The system  100  may also use highly fault-tolerant methods that have been developed to provide a software inter-leaved disk storage methodology that allows one or two hard drives to fail and still not lose target data that is stored on the drives. This software inter-leaved disk storage methodology provides superior fault-tolerance and portability versus other, hardware methodologies, such as RAID-5. 
     The system  100  may also incorporate a methodology that has been developed that allows for a short calibration step just before mission data capture. The calibration methodology step adjusts the camera settings, mainly exposure time, based on sampling the ambient light intensity and setting near optimal values just before reaching the region of interest. A moving average algorithm is then used to make second-by-second camera adjustments in order to deliver improved, consistent photo results. This improves the color processing of the orthomaps. Additionally, the calibration may be used to check or to establish the exact spatial position of each sensor device (cameras, DPG, AMU, EMU, etc.). In this manner, changes that may happen in the spatial location of these devices may be accounted for and maintain overall system precision metrics. 
     Additionally, the system  100  may incorporate a methodology that has been developed that allows for calibrating the precision position and attitude of each sensor device (cameras, DPG, AMU, EMU, etc.) on the vehicle by flying over an area that contains multiple known, visible, highly accurate geographic positions. A program takes this data as input and outputs the micro positional data that is then used to precisely process the orthomaps. 
     In an embodiment of the camera array assembly  300 , the imaging sensors may be arranged in across track, cross-eyed fashion. As depicted in  FIG. 3A , housing  304  comprises a simple enclosure inside of which imaging sensors  306 ,  308 ,  310 ,  312  and  314  are disposed. In a preferred embodiment, the housing  304  may be replaced by a mount unit (not shown) inside of which imaging sensors  306 ,  308 ,  310 ,  312  and  314  are disposed. Whereas  FIG. 3A  depicts a 5-camera array, the system works equally well when utilizing any number of imaging sensors from 1 to any number. Sensors  306  through  314  couple, via the attachment members  318 , either collectively to a single transverse cross member, or individually to lateral cross members disposed between opposing walls of the housing  304 . In alternative embodiments, the housing  304  may itself comprise only a supporting cross member of concave curvature (e.g., a mount unit) to which the imaging sensors  306  through  314  couple, via members  318 . In other embodiments, the housing  304  may comprise a hybrid combination of enclosure and supporting cross member. The housing  304  further comprises an aperture  320  formed in its surface, between the imaging sensors and target  302 . Depending upon the specific type of host craft, the aperture  320  may comprise only a void, or it may comprise a protective screen or window to maintain environmental integrity within the housing  304 . In the event that a protective transparent plate is used for any sensor, special coatings may be applied to the plate to improve the quality of the sensor data. Optionally, the aperture  320  may comprise a lens or other optical device to enhance or alter the nature of the images recorded by the sensors. The aperture  320  is formed with a size and shape sufficient to provide the imaging sensors  306  through  314  proper lines of sight to a target region  322  on terrain  302 . 
     The imaging sensors  306  through  314  are disposed within or along housing  304  such that the focal axes of all sensors converge and intersect each other within an intersection area bounded by the aperture  320 . Depending upon the type of image data being collected, the specific imaging sensors used, and other optics or equipment employed, it may be necessary or desirable to offset the intersection area or point of convergence above or below the aperture  320 . The imaging sensors  306  through  314  are separated from each other at angular intervals. The exact angle of displacement between the imaging sensors may vary widely depending upon the number of imaging sensors utilized and on the type of imaging data being collected. The angular displacement between the imaging sensors may also be unequal, if required, so as to provide a desired image offset or alignment. Depending upon the number of imaging sensors utilized, and the particular configuration of the array, the focal axes of all imaging sensors may intersect at exactly the same point, or may intersect at a plurality of points, all within close proximity to each other and within the intersection area defined by the aperture  320 . 
     As depicted in  FIG. 3A , the imaging sensor  310  is centrally disposed within the housing  304  along axis  316 . The imaging sensor  310  has a focal axis  324 , directed orthogonally from the housing  304  to align the line of sight of the imaging sensor with the image area  326  of the region  322 . The imaging sensor  308  is disposed within the housing  304  along the axis  316 , adjacent to the imaging sensor  310 . The imaging sensor  308  is aligned such that its line of sight coincides with the image area  328  of the region  322 , and such that its focal axis  330  converges with and intersects the axis  324  within the area bounded by the aperture  320 . The imaging sensor  312  is disposed within the housing  304  adjacent to the imaging sensor  310 , on the opposite side of the axis  316  as the imaging sensor  308 . The imaging sensor  312  is aligned such that its line of sight coincides with the image area  332  of the region  322 , and such that its focal axis  334  converges with and intersects axes  324  and  330  within the area bounded by the aperture  320 . The imaging sensor  306  is disposed within the housing  304  along the axis  316 , adjacent to the sensor  308 . The imaging sensor  306  is aligned such that its line of sight coincides with the image area  336  of region  322 , and such that its focal axis  338  converges with and intersects the other focal axes within the area bounded by aperture  320 . The imaging sensor  314  is disposed within housing  304  adjacent to sensor  312 , on the opposite side of axis  316  as sensor  306 . The imaging sensor  314  is aligned such that its line of sight coincides with image area  340  of region  322 , and such that its focal axis  344  converges with and intersects the other focal axes within the area bounded by aperture  320 . 
     The imaging sensors  306  through  314  may comprise a number of digital imaging devices including, for example, individual area scan cameras, line scan cameras, infrared sensors, hyperspectral and/or seismic sensors. Each sensor may comprise an individual imaging device, or may itself comprise an imaging array. The imaging sensors  306  through  314  may all be of a homogenous nature, or may comprise a combination of varied imaging devices. For ease of reference, the imaging sensors  306  through  314  are hereafter referred to as cameras  306  through  314 , respectively. 
     In large-format film or digital cameras, lens distortion is typically a source of imaging problems. Each individual lens must be carefully calibrated to determine precise distortion factors. In one embodiment of this invention, small-format digital cameras having lens angle widths of 17 degrees or smaller are utilized. This alleviates noticeable distortion efficiently and affordably. 
     Cameras  306  through  314  are alternately disposed within housing  304  along axis  316  such that each camera&#39;s focal axis converges upon aperture  320 , crosses focal axis  324 , and aligns its field of view with a target area opposite its respective position in the array resulting in a “cross-eyed”, retinal relationship between the cameras and the imaging target(s). The camera array assembly  300  is configured such that adjoining borders of image areas  326 ,  328 ,  332 ,  336  and  340  overlap slightly. In an embodiment, the adjoining borders of image areas  340  and  332 ,  332  and  326 ,  326  and  328 , and  328  and  336  overlap between about 1% and about 99% of the image area. In another embodiment, the adjoining borders overlap between about 10% and about 80%. In another embodiment, the adjoining borders overlap between about 20% and about 60%. 
     Depending upon the shape and size of imaging sensors  306 ,  308 ,  310 ,  312  and  314 , assembly  300  provides the ability to produce images having customizable FOVs, of a generally circular nature. Depending on the mount unit(s) and imaging sensors utilized, assembly  300  may be deployed to produce stereoscopic images. In alternative embodiments, any number of mount units, containing any number of imaging sensors having various shapes and sizes, may be combined to provide imaging data on any desired target region. 
     In another embodiment of the camera array assembly  300 , the imaging sensors may be arranged in a long-track, cross-eyed fashion. As depicted in  FIG. 3B , housing  304  comprises a simple enclosure inside of which imaging sensors  306 ,  308 ,  310 ,  312  and  314  are disposed. In a preferred embodiment, the housing  304  may be replaced by a mount unit (not shown) inside of which imaging sensors  306 ,  308 ,  310 ,  312  and  314  are disposed. Whereas  FIG. 3B  depicts a 5-camera array, the system works equally well when utilizing any number of camera sensors from 1 to any number. Sensors  306  through  314  couple, via the attachment members  318 , either collectively to a single transverse cross member, or individually to lateral cross members disposed between opposing walls of the housing  304 . In alternative embodiments, the housing  304  may itself comprise only a supporting cross member of concave curvature (e.g., a mount unit) to which the imaging sensors  306  through  314  couple, via members  318 . In other embodiments, the housing  304  may comprise a hybrid combination of enclosure and supporting cross member. The housing  304  further comprises an aperture  320  formed in its surface, between the imaging sensors and target  302 . Depending upon the specific type of host craft, the aperture  320  may comprise only a void, or it may comprise a protective screen or window to maintain environmental integrity within the housing  304 . In the event that a protective transparent plate is used for any sensor, special coatings may be applied to the plate to improve the quality of the sensor data. Optionally, the aperture  320  may comprise a lens or other optical device to enhance or alter the nature of the images recorded by the sensors. The aperture  320  is formed with a size and shape sufficient to provide the imaging sensors  306  through  314  proper lines of sight to a target region  322  on terrain  302 . 
     The imaging sensors  306  through  314  are disposed within or along housing  304  such that the focal axes of all sensors converge within an intersection area bounded by the aperture  320 . Depending upon the type of image data being collected, the specific imaging sensors used, and other optics or equipment employed, it may be necessary or desirable to offset the intersection area or point of convergence above or below the aperture  320 . The imaging sensors  306  through  314  are separated from each other at angular intervals. The exact angle of displacement between the imaging sensors may vary widely depending upon the number of imaging sensors utilized and on the type of imaging data being collected. The angular displacement between the imaging sensors may also be unequal, if required, so as to provide a desired image offset or alignment. Depending upon the number of imaging sensors utilized, and the particular configuration of the array, the focal axes of all imaging sensors may intersect at exactly the same point, or may intersect at a plurality of points, all within close proximity to each other and within the intersection area defined by the aperture  320 . 
     As depicted in  FIG. 3B , the imaging sensor  310  is centrally disposed within the housing  304  along axis  316 . The imaging sensor  310  has a focal axis  324 , directed orthogonally from the housing  304  to align the line of sight of the imaging sensor with the image area  326  of the region  322 . The imaging sensor  308  is disposed within the housing  304  along the axis  316 , adjacent to the imaging sensor  310 . The imaging sensor  308  is aligned such that its line of sight coincides with the image area  328  of the region  322 , and such that its focal axis  330  converges with and intersects the axis  324  within the area bounded by the aperture  320 . The imaging sensor  312  is disposed within the housing  304  adjacent to the imaging sensor  310 , on the opposite side of the axis  316  as the imaging sensor  308 . The imaging sensor  312  is aligned such that its line of sight coincides with the image area  332  of the region  322 , and such that its focal axis  334  converges with and intersects axes  324  and  330  within the area bounded by the aperture  320 . The imaging sensor  306  is disposed within the housing  304  along the axis  316 , adjacent to the sensor  308 . The imaging sensor  306  is aligned such that its line of sight coincides with the image area  336  of region  322 , and such that its focal axis  338  converges with and intersects the other focal axes within the area bounded by aperture  320 . The imaging sensor  314  is disposed within housing  304  adjacent to sensor  312 , on the opposite side of axis  316  as sensor  306 . The imaging sensor  314  is aligned such that its line of sight coincides with image area  340  of region  322 , and such that its focal axis  344  converges with and intersects the other focal axes within the area bounded by aperture  320 . 
     Cameras  306  through  314  are alternately disposed within housing  304  along axis  316  such that each camera&#39;s focal axis converges upon aperture  320 , crosses focal axis  324 , and aligns its field of view with a target area opposite its respective position in the array resulting in a “cross-eyed”, retinal relationship between the cameras and the imaging target(s). The camera array assembly  300  is configured such that adjoining borders of image areas  326 ,  328  and  332  overlap slightly. In an embodiment, the adjoining borders of image areas  326  and  328  and/or  326  and  332  overlap between about 1% and about 99% of the image area. In another embodiment, the adjoining borders overlap between about 30% and about 95%. In another embodiment, the adjoining borders overlap between about 50% and about 90%. 
     The adjoining borders of image areas  328  and  336 , and  332  and  340  may or may not overlap slightly. In an embodiment, the adjoining borders of image areas  328  and  336 , and  332  and  340  overlap between about 0% and about 100%. In another embodiment, the adjoining borders overlap between about 30% to about 95%. In another embodiment, the adjoining borders overlap between about 50% and about 90%. 
     In another embodiment similar to the camera array of  FIG. 3B , the imaging sensors may be arranged in a long-track, cross-eyed fashion as in camera array  2600 . As depicted in  FIG. 26A , mount unit  2604  comprises a simple structure inside of which imaging sensors  2606 ,  2608 ,  2610  and  2612  are disposed. Whereas  FIG. 26A  depicts a 4-camera array, the system works equally well when utilizing any number of imaging sensors from 1 to any number. Sensors  2606  through  2612  couple, via the attachment members  2618 , either collectively to a single transverse cross member, or individually to lateral cross members disposed between opposing walls of the mount unit  2604 . 
     Similar to camera array assembly of  FIG. 3B , the mount unit  2604  further comprises an aperture  2620  formed in its surface, between the imaging sensors and a target (not shown). Depending upon the specific type of host craft, the aperture  2620  may comprise only a void, or it may comprise a protective screen or window to maintain environmental integrity within the mount unit  2604 . In the event that a protective transparent plate is used for any sensor, special coatings may be applied to the plate to improve the quality of the sensor data. Optionally, the aperture  2620  may comprise a lens or other optical device to enhance or alter the nature of the images recorded by the sensors. The aperture  2620  is formed with a size and shape sufficient to provide the imaging sensors  2606  through  2612  proper lines of sight to a target region on terrain (not shown). 
     As discussed with respect to  FIG. 3B , the imaging sensors  2606  through  2614  are disposed within or along a concave curvilinear array axis  2616  in mount unit  2604  such that the focal axes of all sensors converge and intersect each other within an intersection area bounded by the aperture  2620 . Depending upon the type of image data being collected, the specific imaging sensors used, and other optics or equipment employed, it may be necessary or desirable to offset the intersection area or point of convergence above or below the aperture  2620 . The imaging sensors  2606  through  2612  are separated from each other at angular intervals. The exact angle of displacement between the imaging sensors may vary widely depending upon the number of imaging sensors utilized and on the type of imaging data being collected. The angular displacement between the imaging sensors may also be unequal, if required, so as to provide a desired image offset or alignment. Depending upon the number of imaging sensors utilized, and the particular configuration of the array, the focal axes of all imaging sensors may intersect at exactly the same point, or may intersect at a plurality of points, all within close proximity to each other and within the intersection area defined by the aperture  2620 . 
     In another embodiment of the camera array assembly  300 , the imaging sensors may be arranged in a long-track, cross-eyed fashion for mapping infrastructure. As depicted in  FIG. 3C , housing  304  comprises a simple enclosure inside of which imaging sensors  306 ,  308 ,  310 ,  312  and  314  are disposed. In a preferred embodiment, the housing  304  may be replaced by a mount unit (not shown) inside of which imaging sensors  306 ,  308 ,  310 ,  312  and  314  are disposed. Whereas  FIG. 3B  depicts a 5-camera array, the system works equally well when utilizing any number of camera sensors from 3 to any number. As discussed above, sensors  306  through  314  couple, via the attachment members  318 , either collectively to a single transverse cross member, or individually to lateral cross members disposed between opposing walls of the housing  304 . In alternative embodiments, the housing  304  may itself comprise only a supporting cross member of concave curvature (e.g., a mount unit) to which the imaging sensors  306  through  314  couple, via members  318 . In other embodiments, the housing  304  may comprise a hybrid combination of enclosure and supporting cross member. The housing  304  further comprises an aperture  320  formed in its surface, between the imaging sensors and target  302 . Depending upon the specific type of host craft, the aperture  320  may comprise only a void, or it may comprise a protective screen or window to maintain environmental integrity within the housing  304 . In the event that a protective transparent plate is used for any sensor, special coatings may be applied to the plate to improve the quality of the sensor data. Optionally, the aperture  320  may comprise a lens or other optical device to enhance or alter the nature of the images recorded by the sensors. The aperture  320  is formed with a size and shape sufficient to provide the imaging sensors  306  through  314  proper lines of sight to a target region  322  on terrain  302 . 
     Similar to the camera assembly of  FIG. 3B , the imaging sensors  306  through  314  are disposed within or along housing  304  such that the focal axes of all sensors converge within an intersection area bounded by the aperture  320 . Depending upon the type of image data being collected, the specific imaging sensors used, and other optics or equipment employed, it may be necessary or desirable to offset the intersection area or point of convergence above or below the aperture  320 . The imaging sensors  306  through  314  are separated from each other at angular intervals. The exact angle of displacement between the imaging sensors may vary widely depending upon the number of imaging sensors utilized and on the type of imaging data being collected. The angular displacement between the imaging sensors may also be unequal, if required, so as to provide a desired image offset or alignment. Depending upon the number of imaging sensors utilized, and the particular configuration of the array, the focal axes of all imaging sensors may intersect at exactly the same point, or may intersect at a plurality of points, all within close proximity to each other and within the intersection area defined by the aperture  320 . 
     As depicted in  FIG. 3C , the imaging sensor  310  is centrally disposed within the housing  304  along axis  316 . The imaging sensor  310  has a focal axis  324 , directed orthogonally from the housing  304  to align the line of sight of the imaging sensor with the image area  326  of the region  322 . The imaging sensor  308  is disposed within the housing  304  along the axis  316 , adjacent to the imaging sensor  310 . The imaging sensor  308  is aligned such that its line of sight coincides with the image area  328  of the region  322 , and such that its focal axis  330  converges with and intersects the axis  324  within the area bounded by the aperture  320 . The imaging sensor  312  is disposed within the housing  304  adjacent to the imaging sensor  310 , on the opposite side of the axis  316  as the imaging sensor  308 . The imaging sensor  312  is aligned such that its line of sight coincides with the image area  332  of the region  322 , and such that its focal axis  334  converges with and intersects axes  324  and  330  within the area bounded by the aperture  320 . The imaging sensor  306  is disposed within the housing  304  along the axis  316 , adjacent to the sensor  308 . Dissimilar to the camera assembly of  FIG. 3B , the imaging sensor  306  is aligned such that its line of sight coincides with the forward oblique image area  336  of region  322 , and such that its focal axis  338  converges with and intersects the other focal axes within the area bounded by aperture  320 . As depicted in  FIGS. 3C &amp; 4C , imaging sensor  306  captures the forward oblique image area  336  including a rear view of a second tower  346 . The imaging sensor  314  is disposed within housing  304  adjacent to sensor  312 , on the opposite side of axis  316  as sensor  306 . The imaging sensor  314  is aligned such that its line of sight coincides with rear oblique image area  340  of region  322 , and such that its focal axis  344  converges with and intersects the other focal axes within the area bounded by aperture  320 . As depicted in  FIGS. 3C &amp; 4C , imaging sensor  314  captures the forward oblique image area  340  including a front view of a first tower  348 . 
     Similar to the camera assembly of  FIG. 3B , cameras  306  through  314  are alternately disposed within housing  304  along axis  316  such that each camera&#39;s focal axis converges upon aperture  320 , crosses focal axis  324 , and aligns its field of view with a target area opposite its respective position in the array resulting in a “cross-eyed”, retinal relationship between the cameras and the imaging target(s). The camera array assembly  300  is configured such that adjoining borders of image areas  326 ,  328  and  332  overlap slightly. In an embodiment, the adjoining borders of image areas  326  and  328  and/or  326  and  332  overlap between about 1% and about 99% of the image area. In another embodiment, the adjoining borders overlap between about 30% and about 95%. In another embodiment, the adjoining borders overlap between about 50% and about 90%. 
     The adjoining borders of image areas  328  and  336 , and  332  and  340  may or may not overlap slightly. In an embodiment, the adjoining borders of image areas  328  and  336 , and  332  and  340  overlap between about 0% and about 100%. In another embodiment, the adjoining borders overlap between about 30% to about 95%. In another embodiment, the adjoining borders overlap between about 50% and about 90%. 
     Referring to  FIG. 3C-2 , an exemplary sequence of images obtained using a camera array assembly configured in a along track, cross-eyed fashion is depicted. Although the camera array assembly of  FIG. 3C-1  is shown, other along track camera array assemblies may be used.  FIG. 3C-2  illustrates how long track sensors cover infrastructure such as transmission towers, insulators/conductors, transformers and other linearly aligned corridor objects by collecting a sequence of overlapping images that cover front and back sides of long track objects. 
     In an another embodiment of the camera array, the imaging sensors may be arranged in a cross track, cross-eyed and long-track, cross-eyed fashion. See e.g.,  FIGS. 3D &amp; 22A .  FIG. 22A  depicts a concave or retinal camera array assembly  2200  from a rear right side perspective view. Whereas  FIG. 22A  depicts a 15-camera array, the system works equally well when utilizing any number of imaging sensors from 1 to any number. Assembly  2200  is similar in composition, construction and operation to assembly  300 . Assembly  2200  comprises a first imaging array  2202 , a second imaging array  2204  and a third imaging array  2206 . Array  2204  is configured as a primary sensor array, disposed within assembly  2200  such that the focal axis  2208  of its primary imaging sensor  2210  is directed downwardly from assembly  2200 , orthogonal to a target area  2212  along a terrain  2214 . Assembly  2200  is adaptably mountable to a vehicle that moves, with respect to terrain  2214 , along a flight path  2216 . Arrays  2202 ,  2204  and  2206  are configured within assembly  2200  as sub-assemblies of imaging sensors. Array  2202  is offset, with respect to flight path  2216 , in front of mount unit  2204  by angular offset  2218 . Similarly, array  2206  is offset, with respect to flight path  2216 , behind array  2204  by angular offset  2220 . Angular offset  2218  is selected such that focal axis  2222  of primary imaging sensor  2224  disposed within mount unit  2202  is directed downward toward target area  2212  forming angle  2232  with the target surface. Angular offset  2220  is selected such that the focal axis  2228  of primary imaging sensor  2230  disposed within mount unit  2206  is directed downward toward target area  2212  forming angle  2226  with the target surface. Preferably, angular offsets  2218  and  2220  are equal, but they may be different to provide a desired imaging effect. The focal axes of the other individual imaging sensors disposed within mount units  2202 ,  2204  and  2206  form similar angular relationships to the target area  2212  and one another, subject to their relative positions along the mount units. 
     In an embodiment of the camera array, the imaging sensors may be arranged in a alternative along track, cross-eyed and across-track, cross-eyed fashion. See e.g.,  FIGS. 3E &amp; 22B .  FIG. 22B  depicts a concave or retinal camera array assembly  2200  from a front right side perspective view. Array  2204  is configured as a primary sensor array, disposed within assembly  2200  such that the focal axis  2208  of its primary imaging sensor  2210  is directed downwardly from assembly  2200 , orthogonal to a target area  2212  along a terrain  2214 . Mount unit  2202  is offset, with respect to perpendicular  2234  of flight path (or track)  2216 , to the left (viewing the array from the rear) of mount unit  2204  by angular offset  2218 . Similarly, mount unit  2206  is offset, with respect to perpendicular  2234  of flight path  2216 , to the right (viewing the array from the rear) of mount unit  2204  by angular offset  2220 . Angular offset  2218  is selected such that focal axis  2222  of primary imaging sensor  2224  disposed within mount unit  2202  is directed downward toward target area  2212  forming angle  2232  with the target surface. Angular offset  2220  is selected such that the focal axis  2228  of primary imaging sensor  2230  disposed within mount unit  2206  is directed downward toward target area  2212  forming angle  2226  with the target surface. Preferably, angular offsets  2218  and  2220  are equal, but they may be different to provide a desired imaging effect. The focal axes of the other individual imaging sensors disposed within mount units  2202 ,  2204  and  2206  form similar angular relationships to the target area  2212  and one another, subject to their relative positions along the mount units. 
     In another embodiment of the camera array, the imaging sensors may be arranged in a concave or retinal configuration.  FIG. 23  depicts a concave or retinal camera array assembly  2300  from a bottom view. Whereas  FIG. 23  depicts a 25-camera array, the system works equally well when utilizing any number of imaging sensors from 1 to any number. Assembly  2300  comprises a primary compound concave curvilinear mount unit  2302  and a plurality of compound curvilinear mount units  2304  that are formed of a size and curvatures sufficient to offset and arch over or contact mount unit  2302  at various angular intervals. Any number of mount units  2304  may be employed, and may be so numerous as to form a dome structure for mounting sensors. The angular displacement between the mount units  2304  varies depending on the size of the mount units and the desired imaging characteristics. For example, assembly  2300  may comprise two mount units in an orthogonal (i.e., 90°) relationship with one another. Another assembly, having three mount units, may be configured such that the angular displacement between the mount units is 60°. 
     A primary imaging sensor  2306  is centrally disposed along the concave side of mount unit  2302 , with its focal axis directed orthogonally downward from assembly  2300 . A number of imaging sensors  2308  are disposed, in accordance with the teachings of the present invention, along the concave sides of mount units  2302  and  2304  in a “cross-eyed” fashion. The cross-eyed imaging sensors  2308  are alternatively disposed along mount units  2302  and  2304  such that the focal axis of each imaging sensor converges upon the focal axis of imaging sensor  2306  at an intersection area (not shown), and aligns its field of view with a target area opposite its respective position in the array. Depending upon the shape and size of imaging sensors  2308 , assembly  2300  provides the ability to produce images having customizable FOVs, of a generally circular nature. Depending on the mount units and imaging sensors utilized, assembly  2300  may be deployed to produce stereoscopic images. In alternative embodiments, any number of mount units, containing any number of imaging sensors having various shapes and sizes, may be combined to provide imaging data on any desired target region. 
     Referring again to  FIGS. 3A-3E , if the attachment members  318  are of a permanent and fixed nature (e.g., welds), then the spatial relationship between the aperture  320 , the cameras, and their lines of sight remain fixed as will the spatial relationship between image areas  326 ,  328 ,  332 ,  336  and  340 . Such a configuration may be desirable in, for example, a satellite surveillance application where the camera array assembly  300  will remain at an essentially fixed distance from region  322 . The position and alignment of the cameras is set such that areas  326 ,  328 ,  332 ,  336  and  340  provide full imaging coverage of region  322 . If the attachment members  318  are of a temporary or adjustable nature, however, it may be desirable to selectively adjust, either manually or by remote automation, the position or alignment of the cameras so as to shift, narrow or widen areas  326 ,  328 ,  332 ,  336  and  340 —thereby enhancing or altering the quality of images collected by the camera array assembly  300 . 
     In an embodiment, the rigid mount unit may or may not be affixed to a rigid mount plate. The mount unit is any rigid structure to which at least one imaging sensor may be affixed. The mount unit may be a housing, which encloses the imaging sensor, but may be any rigid structure including a brace, tripod, or the like. For the purposes of this disclosure, an imaging sensor means any device capable of receiving and processing active or passive radiometric energy, i.e., light, sound, heat, gravity, and the like, from a target area. In particular, imaging sensors may include any number of digital cameras, including those that utilize a red-blue-green filter, a bushbroom filter, or a hyperspectral filter, LIDAR sensors, infrared sensors, heat-sensing sensors, gravitometers and the like. Imagining sensors do not include attitude measuring sensors such as gyroscopes, GPS devices, and the like devices, which serve to orient the vehicle with the aid of satellite data and/or inertial data. Preferably, the multiple sensors are different. 
     In another embodiment, single, i.e., at least one, rigid mount unit may be affixed to the same rigid mount plate. 
     In another embodiment, multiple, i.e., at least two, rigid mount units may be affixed to the same rigid mount plate. 
     In the embodiment wherein the imaging sensor is a camera, LIDAR, or the like imaging sensor, the mount unit preferably has an aperture through which light and/or energy may pass. The mount plate is preferably planer, but may be non-planer. In the embodiment, wherein the imaging sensor is a camera, LIDAR, or the like imaging sensor, the mount plate preferably has aperture(s) in alignment with the aperture(s) of the mount unit(s) through which light and/or energy may pass. 
     A rigid structure is one that flexes less than about 100 th  of a degree, preferably less than about 1,000 th  of a degree, more preferably less than about 10,000 th  of a degree while in use. Preferably, the rigid structure is one that flexes less than about 100 th  of a degree, preferably less than about 1,000 th  of a degree, more preferably less than about 10,000 th  of a degree while secured to an aircraft during normal, i.e., non-turbulent, flight. Objects are rigidly affixed to one another if during normal operation they flex from each other less than about 100 th  of a degree, preferably less than about 1,000 th  of a degree, more preferably less than about 10,000 th  of a degree. 
     Camera  310  is designated as the principal camera. The image plane  326  of camera  310  serves as a plane of reference. The orientations of the other cameras  306 ,  308 ,  312  and  314  are measured relative to the plane of reference. The relative orientations of each camera are measured in terms of the yaw, pitch and roll angles required to rotate the image plane of the camera to become parallel to the plane of reference. The order of rotations is preferably yaw, pitch, and roll. 
     The imaging sensors affixed to the mount unit(s) may not be aligned in the same plane. Instead, the angle of their mount relative to the mount angle of a first sensor affixed to the first mount unit, preferably the principle nadir camera of the first mount unit, may be offset. Accordingly, the imaging sensors may be co-registered to calibrate the physical mount angle offset of each imaging sensor relative to each other. In an embodiment, multiple, i.e., at least two, rigid mount units are affixed to the same rigid mount plate and are co-registered. In an embodiment, the cameras  306  through  314  are affixed to a rigid mount unit and co-registered. In this embodiment, the geometric centerpoint of the AMU, preferably a gyroscope, is determined using GPS and inertial data. The physical position of the first sensor affixed to the first mount unit, preferably the principle nadir camera of the first mount unit, is calculated relative to a reference point, preferably the geometric centerpoint of the AMU. Likewise, the physical position of all remaining sensors within all mount units are calculated—directly or indirectly—relative to the same reference point. 
     The boresight angle of a sensor is defined as the angle from the geometric center of that sensor to a reference plane. Preferably the reference plane is orthogonal to the target area. The boresight angle of the first sensor may be determined using the ground target points. The boresight angles of subsequent sensors are preferably calculated with reference to the boresight angle of the first sensor. The sensors are preferably calibrated using known ground targets, which are preferably photo-identifiable, and alternatively calibrated using a self-locking flight path or any other method as disclosed in U.S. Publication No. 2004/0054488A1, now U.S. Pat. No. 7,212,938B2, the disclosure of which is hereby incorporated by reference in full. 
     The imaging sensor within the second mount unit may be any imaging sensor, and is preferably a LIDAR. Alternative, the second imaging sensor is a digital camera, or array of digital cameras. In an embodiment, the boresight angle of the sensor(s) affixed to the second mount unit are calculated with reference to the boresight angle of the first sensor. The physical offset of the imaging sensor(s) within the second mount unit may be calibrated with reference to the boresight angle of the first sensor within the first mount unit. 
     In this manner, all of the sensors are calibrated at substantially the same epoch, using the same GPS signal, the same ground target(s), and under substantially the same atmospheric conditions. This substantially reduces compounded error realized when calibrating each sensor separately, using different GPS signals, against different ground targets, and under different atmospheric conditions. 
     Referring now to  FIGS. 4A-4E , images of areas  336 ,  328 ,  326 ,  332  and  340  taken by cameras  306  through  314 , respectively, are illustrated from an overhead view. Again, because of the “cross-eyed” arrangement, the image of area  336  is taken by camera  306 , the image of area  340  is taken by camera  314 , and so on. In one embodiment of the present invention, images other than those taken by the center camera  310  take on a trapezoidal shape after perspective transformation. See e.g.,  FIG. 4A . Cameras  306  through  314  form an array along axis  316  that is, in most applications, pointed down vertically. In an embodiment, cameras  308 ,  310  and  312  form an ortho array along axis  316  that is pointed down vertically, and cameras  306  and  314  form an oblique array also along axis  316 . See e.g.,  FIGS. 4B &amp; 4C . For example, infrastructure information such as condition of insulators/conductors, transformers transmission lines and transmission towers and location of ground vegetation, trees/foliage, fences and roads with respect to such structures may be obtained. In an alternative embodiment, a second array of cameras, configured similar the array of cameras  306  through  314 , is aligned with respect to the first array of cameras to have an oblique view providing a “heads-up” perspective. The angle of declination from horizontal of the heads-up camera array assembly may vary due to mission objectives and parameters but angles of 25-45 degrees are typical. Other alternative embodiments, varying the mounting of camera arrays, are similarly comprehended by the present invention. See e.g.,  FIGS. 4D &amp; 4E . In all such embodiments, the relative positions and attitudes of the cameras are precisely measured and calibrated so as to facilitate image processing in accordance with the present invention. 
     In one embodiment of the present invention, an external mechanism (e.g., a GPS timing signal) is used to trigger the cameras simultaneously thereby capturing an array of input images. A mosaicing module then renders the individual input images from such an array into an ortho-rectified compound image (or “mosaic”), without any visible seams between the adjacent images. The mosaicing module performs a set of tasks comprising: determining the geographical boundaries and dimensions of each input image; projecting each input image onto the mosaic with accurate geographical positioning; balancing the color of the images in the mosaic; and blending adjacent input images at their shared seams. The exact order of the tasks performed may vary, depending upon the size and nature of the input image data. In certain embodiments, the mosaicing module performs only a single transformation to an original input image during mosaicing. That transformation can be represented by a 4×4 matrix. By combining multiple transformation matrices into a single matrix, processing time is reduced and original input image sharpness is retained. 
     During mapping of the input images to the mosaic, especially when mosaicing is performed at high resolutions, pixels in the mosaic (i.e., output pixels) may not be mapped to by any pixels in the input images (i.e., input pixels). Warped lines could potentially result as artifacts in the mosaic. Certain embodiments of the present invention overcome this with a super-sampling system, where each input and output pixel is further divided into an n×m grid of sub-pixels. Transformation is performed from sub-pixels to sub-pixels. The final value of an output pixel is the average value of its sub-pixels for which there is a corresponding input sub-pixel. Larger n and m values produce mosaics of higher resolution, but do require extra processing time. 
     During its processing of image data, the mosaicing module may utilize the following information: the spatial position (e.g., x, y, z coordinates) of each camera&#39;s focal point at the time an input image is captured; the attitude (i.e., yaw, pitch, roll) of each camera&#39;s image plane relative to the target region&#39;s ground plane at the time an input image was captured; each camera&#39;s fields of view (i.e., along track and cross track); and the Digital Terrain Model (DTM) of the area. The attitude can be provided by the AMUs associated with the system. Digital terrain models (DTMs) or Digital surface models (DSMs) can be created from information obtained using a LIDAR module  118 . LIDAR is similar to the more familiar radar, and can be thought of as laser radar. In radar, radio waves are transmitted into the atmosphere that scatters some of the energy back to the radar&#39;s receiver. LIDAR also transmits and receives electromagnetic radiation, but at a higher frequency since it operates in the ultraviolet, visible and infrared region of the electromagnetic spectrum. In operation, LIDAR transmits light out to a target area. The transmitted light interacts with and is changed by the target area. Some of this light is reflected/scattered back to the LIDAR instrument where it can be analyzed. The change in the properties of the light enables some property of the target area to be determined. The time for the light to travel out to the target area and back to LIDAR device is used to determine the range to the target. 
     DTM and DSM data sets can also be captured from the camera array assembly. Traditional means of obtaining elevation data may also be used such as stereographic techniques. 
     There are presently three basic types of LIDAR: Range finders, Differential Absorption LIDAR (DIAL) and Doppler LIDAR. Range finder LIDAR is the simplest LIDAR and is used to measure the distance from the LIDAR device to a solid or hard target. DIAL LIDAR is used to measure chemical concentrations (such as ozone, water vapor, pollutants) in the atmosphere. A DIAL LIDAR uses two different laser wavelengths that are selected so that one of the wavelengths is absorbed by the molecule of interest while the other wavelength is not. The difference in intensity of the two return signals can be used to deduce the concentration of the molecule being investigated. Doppler LIDAR is used to measure the velocity of a target. When the light transmitted from the LIDAR hits a target moving towards or away from the LIDAR, the wavelength of the light reflected/scattered off the target will be changed slightly. This is known as a Doppler-shift and therefore Doppler LIDAR. If the target is moving away from the LIDAR, the return light will have a longer wavelength (sometimes referred to as a red shift), if moving towards the LIDAR the return light will be at a shorter wavelength (blue shifted). The target can be either a hard target or an atmospheric target (e.g. microscopic dust and aerosol particles that are carried by the wind. 
     A camera&#39;s focal point is preferably used as a perspective transformation center. Its position in space may be determined, for example, by a multi-frequency carrier phase post-processed GPS system mounted on the host craft. The offsets, in three dimensions, of a camera&#39;s focal point are preferably carefully measured against the center of the GPS antenna. These offsets may be combined with the position of the GPS antenna, and the orientation of the host craft, to determine the exact position of the camera&#39;s focal point. The position of the GPS antenna is preferably determined by processing of collected GPS data against similar ground-based GPS antennas deployed at precisely surveyed points. 
     One or more AMUs (e.g., the Applanix POS AV) are preferably mounted onboard for attitude determination. The attitude of the AMU reference plane relative to the target region&#39;s ground plane is preferably measured and recorded at short intervals, with accuracy better than one-hundredth of one degree. The attitude of the AMU reference plane may be defined as the series of rotations that can be performed on the axes of this plane to make it parallel to the ground plane. The term “align” could also be used to describe this operation. 
     The attitude of center camera  310  (i.e. its image plane), relative to the AMU, is preferably precisely calibrated. The attitude of each of the other cameras, relative to center camera  310 , is preferably also be carefully calibrated. This dependent calibration is more efficient than directly calibrating each camera. When the camera array assembly  300  is remounted, only center camera  310  needs to be recalibrated. Effectively, a series of two transformations is applied to an input image from center camera  310 . First, the center camera&#39;s image plane is aligned to the AMU plane. Then, the AMU plane is aligned again to the ground plane. These transformations, however, combine into a single operation by multiplying their respective transformation matrices. For images from each of the other cameras, an additional transformation is first performed to align it with the center camera&#39;s image plane. 
     The position of the focal point of center camera  310  may be determined as described above. The x and y components of this position preferably determine the position of the mosaic&#39;s nadir point  400  on the ground. Field of view (FOV) angles of each camera are known, thus the dimensions of each input image may be determined by the z component of that camera&#39;s focal point. An average elevation of the ground is preferably determined by computing the average elevation of points in the DTMs of the area, and then each input image is projected to an imaginary horizontal plane at this elevation. Relief displacement is then preferably applied using the DTMs of the area. The DTMs can be obtained from many sources including: the USGS 30- or 10-meter DTMs available for most of the US; commercial DTMs; or DTMs obtained by a LIDAR or SAR EMU device mounted on the host craft that captures data concurrently with the cameras. 
     Besides being geographically correctly placed, the resulting compound image also needs to have radiometric consistency throughout, and no visible seams at the joints between two adjacent images. The present invention provides a number of techniques for achieving this goal. 
     A characteristic of a conventional camera is the exposure time (i.e., the time the shutter is open to collect light onto the image plane). The longer the exposure time, the lighter the resultant image becomes. Exposure time must adapt to changes in ambient lighting caused by conditions such as: cloud coverage; the angle and position of the sun relative to the camera; and so forth. Optimal exposure time may also depend on a camera&#39;s orientation with respect to lighting sources (e.g., cameras pointing towards a sunlit object typically receive more ambient light than those pointing towards a shaded object). Exposure time is adjusted to keep the average intensity of an image within a certain desired range. For example, in 24-bit color images each Red, Green and Blue component can have intensity values from 0 to 255. In most instances, however, it is desirable to keep the average intensity at a mean value (i.e., 127). 
     In the present invention, an exposure control module controls exposure time for each of the cameras or imaging sensors. It examines each input image and calculates average image intensity. Based on a moving average (i.e., average intensity of the last X number of images), the exposure control module determines whether to increase or decrease exposure time. The module can use a longer running average to effect a slower reaction to changes in lighting conditions, with less susceptibility to unusually dark or light images (e.g., asphalt roads or water). The exposure control module controls exposure time for each camera separately. 
     In systems where cameras are mounted without forward-motion compensation mechanisms, there must be a maximum limit for exposure time. Setting exposure time to a value larger than the maximum may cause motion-induced blurriness. For example, assume cameras are mounted on an airplane traveling at 170 miles/hour (or about 3 inches/ms). Assume desired pixel resolution is 6 inches. Forward motion during image capture should be limited to half a pixel size—which in this case equals 3 inches. Thus, maximum exposure for example is 1 millisecond. 
     In controlling imaging quality, it is useful to be able to determine if changes in light intensity are caused either due to a change in ambient light or due to the presence of unusually light or dark objects (e.g., reflecting water body, metal roofs, asphalts, etc.). Certain applications of this invention involve aerial photography or surveillance. It is observed that aerial images of the ground usually contain plants and vegetation—which have more consistent reflectivity than water bodies or man-made structures such as roads and buildings. Of course, images of plants and vegetation are usually green-dominant (i.e., the green component is the greatest of the red, green and blue values). Therefore, intensity correlation can be made more accurate by focusing on the green-dominant pixels. 
     The exposure control module computes the average intensity of an image by selecting only green-dominant pixels. For example, if an image has 1 million pixels and 300,000 are green-dominant, only those 300,000 green-dominant pixels are included in the calculation of average intensity. This results in an imaging process that is less susceptible to biasing caused by man-made structures and water bodies, whose pixels are usually not green-dominant. As previously noted, it is desirable to maintain an intensity value of about 127. When intensity value is over 127 (i.e., over-exposed), exposure time is reduced so that less light is captured. Similarly, when intensity value is under 127 (i.e., under-exposed), exposure time is increased so that more light is captured. For example, consider a system flying over a target terrain area having many white roofs, whose intensities are very high. Average intensity for the images captured would tend to be high. In most conventional systems, exposure time would by reduced in order to compensate. In such an example, however, reducing exposure time is not proper, because the average intensity of the images has been biased by the bright roofs. Reducing exposure time would result in images where the ground is darker than it should be. In contrast, if only green-dominant pixels are processed in accordance with the present invention, then pixels representing the overly bright roofs do bias the average intensity and the exposure time is not changed. 
     Thus, the exposure control module reduces intensity differences between input images. Nonetheless, further processing is provided to enhance tonal balance. There are a number of factors (e.g., lens physics, atmospheric conditions, spatial/positional relationships of imaging devices) that cause an uneven reception of light from the image plane. More light is received in the center of a camera or sensor than at the edges. 
     The mosaicing module of the present invention addresses this with an anti-vignetting function, illustrated in reference now to  FIG. 5 . A number of focal columns  500 ,  502 ,  504 ,  506  and  508  converge from image plane  509  and cross through focal point  510  as they range across imaging target area  512  (e.g., ground terrain). Columns  500  through  508  may comprise individual resolution columns of a single camera or sensor, or may represent the focal axes of a number of independent cameras or sensors. For reference purposes, column  504  serves as the axis and point  513  at which column  504  intersects image plane  509  serves as a principal point. The exposure control module applies an anti-vignetting function multiplying the original intensity of an input pixel with a column-dependent anti-vignetting factor. Because the receiving surface is represented as a plane with a coordinate system, each column will have a number of resolution rows (not shown). This relationship may be expressed, for a pixel p at column x and row y, as follows:
 
&lt;adjusted intensity&gt;=&lt;original intensity&gt;*ƒ( x );
 
where ƒ(x) is a function of the form:
 
ƒ( x )=cos(off-axis angle)**4.
 
The off-axis angle  514  is: zero for center column  504 ; larger for columns  502  and  506 ; and larger still for columns  500  and  508 . The overall field of view angle  516  (FOVx angle) is depicted between columns  504  and  508 .
 
     The function ƒ(x) can be approximated by a number of line segments between columns. For a point falling within a line segment between any given columns c 1  and c 2 , an adjustment factor is computed as follows:
 
&lt;adjustment factor for  c &gt;=ƒ( c 1)+[ƒ( c 2)−ƒ( c 1)*( c−c 1)/( c 2 −c 1)];
 
where ƒ(c 1 ) and ƒ(c 2 ) are the ƒ function values of the off-axis angles at column c 1  and c 2 , respectively.
 
     Each set of input images needs to be stitched into a mosaic image. Even though the exposure control module regulates the amount of light each camera or sensor receives, the resulting input images may still differ in intensity. The present invention provides an intensity-balancing module that compares overlapping area between adjacent input images, to further balance the relative intensities. Because adjoining input images are taken simultaneously, the overlapping areas should, in theory, have identical intensity in both input images. However, due to various factors, the intensity values are usually not the same. Some such factors causing intensity difference could include, for example, the exposure control module being biased by unusually bright or dark objects present in the field of view of only a particular camera, or the boresight angles of cameras being different (i.e., cameras that are more slanted receive less light than those more vertical). 
     To balance two adjacent images, one is chosen as the reference image and the other is the secondary image. A correlation vector (fR, fG, FB) is determined using, for example, the following process. Let V be a 3×1 vector representing the values (R, G and B) of a pixel: 
             V   =           R           G           B         .           
A correlation matrix C may be derived as:
 
               C   =         FR       0       0           0       FG       0           0       0       FB           ;         
where FR=AvgIr/AvgIn; AvgIr=Red average intensity of overlapped region in reference image; AvgIn=Red average intensity of overlapped region in new image; and FG and FB are similarly derived.
 
     The correlation matrix scales pixel values of the secondary image so that the average intensity of the overlapping area of the secondary image becomes identical to the average intensity of the overlapping area of the reference image. The second image can be balanced to the reference image by multiplying its pixel values by the correlation matrix. 
     Thus, in one embodiment of a balancing process according to the present invention, a center image is considered the reference image. The reference image is first copied to the compound image (or mosaic). Overlapping areas between the reference image and an adjoining image (e.g., the near left image) are correlated to compute a balancing correlation matrix (BCM). The BCM will be multiplied with vectors representing pixels of the adjoining image to make the intensity of the overlapping area identical in both images. One embodiment of this relationship may be expressed as:
 
Let  I (center)=Average intensity of overlapping area in center image;
 
 I (adjoining)=Average intensity of overlap in adjoining image; then
 
Balancing factor= I (center)/ I (adjoining)
 
     The balancing factor for each color channel (i.e., red, green and blue) is independently computed. These three values form the BCM. The now-balanced adjoining image is copied to the mosaic. Smooth transitioning at the border of the copied image is providing by “feathering” with a mask. This mask has the same dimension as the adjoining image and comprises a number of elements. Each element in the mask indicates the weight of the corresponding adjoining image pixel in the mosaic. The weight is zero for pixels at the boundary (i.e. the output value is taken from the reference image), and increases gradually in the direction of the adjoining image until it becomes unity—after a chosen blending width has been reached. Beyond the blending area, the mosaic will be entirely determined by the pixels of the adjoining image. Similarly, the overlaps between all the other constituent input images are analyzed and processed to compute the correlation vectors and to balance the intensities of the images. 
     A correlation matrix is determined using, for example, the following process with reference to  FIG. 6 .  FIG. 6  depicts a strip  600  being formed in accordance with the present invention. A base mosaic  602  and a new mosaic  604 , added along path (or track)  606 , overlap each other in region  608 . Let V be a vector that represents the R, G and B values of a pixel: 
             V   =         R           G           B               
Let h be the transition width of region  608 , and y be the along-track  606  distance from the boundary  610  of the overlapped region to a point A, whose pixel values are represented by V. Let C be the correlation matrix:
 
             C   =         FR       0       0           0       FG       0           0       0       FB               
The balanced value of V, called V′ is:
 
 V′=[y/h·I +(1 −y/h )· C]×V , for 0 &lt;y&lt;h;  
 
 V′=V , for  y&gt;=h;  
 
where I is the identity matrix
 
             1   =           1       0       0           0       1       0           0       0       1         .           
Note that the “feathering” technique is also used in combination with the gradient to minimize seam visibility.
 
     When mosaics are long, differences in intensity at the overlap may change from one end of the mosaic to the other. Computing a single correlation vector to avoid creating visible seams may not be possible. The mosaic can be divided into a number of segments corresponding to the position of the original input images that make up the mosaic. The process described above is applied to each segment separately to provide better local color consistency. 
     Under this refined algorithm, pixels at the border of two segments may create vertical seams (assuming north-south flight lines). To avoid this problem, balancing factors for pixels in this area have to be “transitioned” from that of one segment to the other. This is explained now with reference to  FIG. 7 . 
       FIG. 7  depicts a strip  700  being formed in accordance with the present invention. A base mosaic  702  and a new segment  704  overlap in area  706 . Mosaic  702  and another new segment  708  overlap in area  710 . Segments  704  and  708  overlap in area  712 , and areas  706 ,  710  and  712  all overlap and coincide at area  714 . For explanation purposes, point  716  serves as an origin for y-axis  718  and x-axis  720 . Movement along y-axis  718  represents movement along the flight path of the imaging system. Point  716  is located at the lower left of area  714 . 
     According to the present invention, the dimensions of a strip are determined by the minimum and maximum x and y values of the constituent mosaics. An output strip is initialized to a background color. A first mosaic is transferred to the strip. The next mosaic (along the flight path) is processed next. Intensity values of the overlapping areas of the new mosaic and the first mosaic are correlated, separately for each color channel. The new mosaic is divided into a number of segments corresponding to the original input images that made up the mosaic. A mask matrix, comprising a number of mask elements, is created for the new mosaic. A mask element contains the correlation matrix for a corresponding pixel in the new mosaic. All elements in the mask are initialized to unity. The size of the mask can be limited to just the transition area of the new mosaic. The correlation matrix is calculated for the center segment. The mask area corresponding to the center segment is processed. The values of the elements at the edge of the overlap area are set to the correlation vector. Then, gradually moving away from the first mosaic along the strip, the components of the correlation matrix are either increased or decreased (whether they are less or more than unity, respectively) until they become unity at a predetermined transition distance. The area of the mask corresponding to a segment adjoining the center segment is then processed similarly. However, the area  714  formed by the first mosaic and the center and adjoining segments of the new image requires special treatment. Because the correlation matrix for the adjoining segment may not be identical to that of the center segment, a seam may appear at the border of the two segments in the overlap area  714  with the first mosaic. Therefore, the corner is influenced by the correlation matrices from both segments. For a mask cell A at distance x to the border with the center segment and distance y to the overlap edge, its correlation matrix is the distance-weighted average of the two segments, evaluated as follows: 
     For pixel A(x, y) in area  714  at distance x to the border with the center segment, its balanced values are computed as the distance-weighted averages of the values computed using the two segments; 
     V 1  is the balanced RGB vector based on segment  704 ; 
     V 2  is the balanced RGB vector based on segment  708 ; 
     V′ is the combined (final) balanced RGB vector
 
 V ′=(( d−x )/ d )· V   1 +( x/d )· V 2;
 
where
         x-axis is the line going through bottom of overlapped region;   y-axis is the line going through the left side of the overlapped region between segments  704  and  708 ;   h is the transition width; and   d is the width of the overlapped region between segments  704  and  708 .
 
The mask areas corresponding to other adjoining segments are computed similarly.
       

     Further according to the present invention, a color fidelity (i.e., white-balance) filter is applied. This multiplies R and B components with a determinable factor to enhance color fidelity. The factor may be determined by calibrating the cameras and lenses. The color fidelity filter ensures that the colors in an image retain their fidelity, as perceived directly by the human eye. Within the image capture apparatus, the Red, Green and Blue light receiving elements may have different sensitivities to the color they are supposed to capture. A “while-balance” process is applied—where image of a white object is captured. Theoretically, pixels in the image of that white object should have equivalent R, G and B values. In reality, however, due to different sensitivities and other factors, the average color values for each R, G and B may be avgR, avgG and avgB, respectively. To equalize the color components, the R, G and B values of the pixels are multiplied by the following ratios: 
     R values are multiplied by the ratio avgG/avgR; and 
     B values are multiplied by the ratio avgG/avgB. 
     The end result is that the image of the white object is set to have equal R G B components. 
     In most applications, a strip usually covers a large area of non-water surface. Thus, average intensity for the strip is unlikely to be skewed by anomalies such as highly reflecting surfaces. The present invention provides an intensity normalization module that normalizes the average intensity of each strip so that the mean and standard deviation are of a desired value. For example, a mean of 127 is the norm in photogrammetry. A standard deviation of 51 helps to spread the intensity value over an optimal range for visual perception of image features. Each strip may have been taken in different lighting conditions and, therefore, may have different imaging data profiles (i.e., mean intensity and standard deviation). This module normalizes the strips, such that all have the same mean and standard deviation. This enables the strips to be stitched together without visible seams. 
     This intensity normalization comprises a computation of the mean intensity for each channel R, G and B, and for all channels. The overall standard deviation is then computed. Each R, G and B value of each pixel is transformed to the new mean and standard deviation:
 
new value=new mean+(old value−old mean)*(new std/old std).
 
     Next, multiple adjacent strips are combined to produce tiled mosaics for an area of interest. Finished tiles can correspond to the USGS quads or quarter-quads. Stitching strips into mosaics is similar to stitching mosaics together to generate strips, with strips now taking the role of the mosaics. At the seam line between two strips, problems may arise if the line crosses elevated structures such as buildings, bridges, etc. This classic problem in photogrammetry arises from the parallax caused by the same object being looked at from two different perspectives. During imaging of a building, for example, one strip may present a view from one side of the building while another strip presents a view from another side of the building. After the images are stitched together, the resulting mosaic may look like a tepee. In order to address this, a terrain-guided mosaicing process may be implemented to guide the placement of a seam line. For example, LIDAR or DEM data collected with, or analyzed from, image data may be processed to determine the configuration and shaping of images as they are mosaiced together. Thus, in some mosaiced images, a seam line may not be a straight line—instead comprising a seam line that shifts back and forth to snake through elevated structures. 
     Referring now to  FIG. 8 , one embodiment of an imaging process  800  is illustrated in accordance with the present invention as described above. Process  800  begins with a series  802  of one, or more, raw collected images. Images  802  are then processed through a white-balancing process  804 , transforming them into a series of intermediate images. Series  802  is then processed through anti-vignetting function  806  before progressing to the orthorectification process  808 . As previously noted, orthorectification may rely on position and attitude data  810  from the imaging sensor system or platform, and on DTM data  812 . DTM data  812  may be developed from position data  810  and from, for example, USGS DTM data  814  or LIDAR data  816 . Series  802  is now ortho-rectified and processing continues with color balancing  818 . After color balancing, series  802  is converted by mosaicing module  820  into compound image  822 . Module  820  performs the mosaicing and feathering processes during this conversion. Now, one or more compound images  822  are further combined in step  824 , by mosaicing with a gradient and feathering, into image strip  826 . Image strips are processed through intensity normalization  828 . The now normalized strips  828  are mosaiced together in step  830 , again by mosaicing with a gradient and feathering, rendering a finishing tiled mosaic  832 . The mosaicing performed in step  830  may comprise a terrain-guided mosaicing, relying on DTM data  812  or LIDAR data  816 . 
       FIG. 9  illustrates diagrammatically how photos taken with the camera array assembly may be aligned to make an individual frame. This embodiment shows a photo patter illustration looking down from a vehicle, using data ortho-rectified from five cameras. 
       FIG. 10  is a block diagram of the processing logic according to certain embodiments of the present invention. As shown in block diagram  1000 , the processing logic accepts one or more inputs, which may include elevation measurements  1002 , attitude measurements  1004  and/or photo and sensor imagery  1006 . Certain inputs may be passed through an initial processing step prior to analysis, as is shown in block  1008 , wherein the attitude measurements are combined with data from ground control points. Elevation measurements  1002  and attitude measurements  1004  may be combined to generate processed elevation data  1010 . Processed elevation data  1010  may then be used to generate elevation DEM  1014  and DTM  1016 . Similarly, attitude measurements  1006  may be combined with photo and sensor imagery  1006  to generate georeferenced images  1012 , which then undergo image processing  1018 , which may include color balancing and gradient filtering. 
     Depending on the data set to be used ( 1020 ), either DTM  1016  or a USGS DEM  1022  is combined with processed images  1018  to generate ortho-rectified imagery  1024 . Ortho-rectified imagery  1024  then feeds into self-locking flight lines  1026 . Balancing projection mosaicing  1028  then follows, to generate final photo output  1030 . 
     The present invention may capture ortho and/or oblique image data using a camera array as depicted in  FIGS. 3A-3E ,  22 A- 22 B and  23  and may merge the image data with attitude and positional measurements in order to create a three dimensional image (i.e., 3D point cloud) or digital elevation model (DEM). As discussed above, the 3D point cloud or DEM is a representation of the ground surface including man-made structures. The 3D point cloud or DEM may be calculated using stereographic techniques from the image data, or, alternatively, provided directly by LIDAR or an existing DEM. The 3D point cloud or DEM may be calculated from any overlapping image data from a single camera overlapping in time or overlapping image data from any two cameras overlapping in space and/or time. The sequence of overlapping images may be ortho-rectified using standard photogrammetry techniques to produce an orthomap in which each pixel has an unique latitude and longitude coordinate as discussed above and a unique elevation coordinate. 
     To calculate the elevation of an object, overlapping ortho and/or oblique images (i.e., stereo images) are needed to determine a stereoscopic parallax and create a stereo/three dimensional view as depicted in  FIG. 24 . The overlapping images may be obtained from a single camera overlapping in time or from any two cameras overlapping in space and/or time.  FIG. 24  depicts a sequence of overlapping oblique images obtained from an oblique camera array at two different times. Although the camera array assembly of  FIG. 24  is shown, other ortho and/or oblique camera array assemblies may be used. The adjoining borders of image area  2402  and  2404  should overlap slightly. In an embodiment, the adjoining borders of image areas  2402  and  2404  overlap between about 1% and about 100%. In another embodiment, the adjoining borders of image areas  2402  and  2404  overlap between about 20% to about 70%. In another embodiment, the adjoining borders of image areas  2402  and  2404  overlap between about 50% and about 70%. In another embodiment, a sidelap area  2406  overlaps between about 20% and about 30% and the forelap area  2408  overlaps about 50% to about 70%. 
     The elevation of an object may be calculated using standard stereographic techniques from overlapping ortho and/or oblique images or, alternatively, it may be obtained directly from LIDAR or a pre-existing DEM as described below. The principal point of each image (e.g., principal point  2502  of image  2402 ) is located using direct calculation techniques that determine virtual corner fiduciary coordinates after correcting for yaw, pitch and roll by the AMU or aerial triangularization techniques (e.g., upper left and lower right (UL-LR) and upper right and lower left (UR-LL)) as depicted in  FIG. 25 . The conjugate principal point, which is the principal point of the adjacent image is similarly located. The sequence of oblique images are aligned so that the principal points and conjugate principal points are on a straight line. The line between the principal point and the conjugate principal point is the flight path (or track). 
     An images central projection result in an image displacement where objects are shifted or displaced from their correct positions. Relief displacement is due to differences in the relative elevations of objects from a specific ground plane. All objects that extend above or below a specified ground plane will exhibit image displacement in proportion to the object&#39;s height. The taller the object, the greater the relief displacement. Tall objects can exhibit image displacement even from great altitude. 
     Radial displacement is due differences in the relative distance of objects from the principal point. All objects that are away from the principal point will exhibit radial displacement in proportion to the object&#39;s distance from the point. The larger the distance, the greater the radial displacement. 
     A stereoscopic parallax is caused by capturing images of the same object from different points of view along the flight path. The elevation of an object may be calculated using stereoscopic parallax:
 
 h =( H ′)* dP /( P+dP )
 
where
         h is the object&#39;s elevation of height   H′ is the flight altitude;   dP is the differential parallax;   P is the average image base length.       

     Alternatively, the elevation of the object may be calculated using overlapping oblique images:
 
 h=d *( H ′)/ r  
 
where
         h is the object elevation of height;   H′ is flight altitude which may be obtained by multiplying the representative fraction by the focal length of the camera;   d is the length of an object from base to top; and   r is the distance from the principal point to top of object.       

     The present invention may employ a certain degree of lateral oversampling to improve output quality.  FIG. 11  is an illustration of a lateral oversampling pattern  1100  looking down from a vehicle according to certain embodiments of the present invention showing minimal lateral oversampling. In this illustration, the central nadir region  1102  assigned to the center camera overlaps only slightly with the left nadir region  1104  and right nadir region  1106 , so that overlap is minimized.  FIG. 12  is an illustration of a lateral oversampling pattern  1200  looking down from a vehicle according to certain embodiments of the present invention showing a greater degree of lateral oversampling. In this illustration, the central nadir region  1202  shows a high degree of overlap with left nadir region  1204  and right nadir region  1206 . 
     In addition to the use of lateral oversampling as shown in  FIGS. 11 &amp; 12 , the present invention may employ flight line oversampling as well.  FIG. 13  is an illustration of a flight line oversampling pattern  1300  looking down from a vehicle according to certain embodiments of the present invention showing a certain degree of flight line oversampling but minimal lateral oversampling. Central nadir regions  1302  and  1304  are overlapped to one another along the flight line, but do not overlap laterally with left nadir regions  1306  and  1308  or with right nadir regions  1310  and  1312 . 
       FIG. 14  is an illustration of flight line oversampling looking down from a vehicle according to certain embodiments of the present invention showing significant flight line oversampling as well as significant lateral oversampling. It can be seen that each of the central nadir regions  1402  through  1406  are significantly overlapped with one another as well as with left nadir regions  1408  through  1412  and right nadir regions  1414  through  1418 . Left nadir regions  1408  through  1412  are overlapped with one another, as are right nadir regions  1414  through  1418 . Accordingly, each point on the surface is sampled at least twice, and in some cases as many as four times. This technique uses the fact that in the area of an image that is covered twice, or more, by different camera sensors, a doubling of the image resolution is possible in both the lateral (across path) and flight line (along path) directions for an overall quadrupling of the resolution. In practice, the improvement in image/sensor resolution is somewhat less than doubled in each of the dimensions, approximately 40% in each dimension, or 1.4×1.4=˜2 times. This is due to the statistical variations of the sub-pixel alignment/orientation. In effect, the pixel grid is rarely exactly equidistant from the overlaid pixel grid. If extremely precise lateral camera sensor alignments were made at the sub-pixel level, a quadrupling of image resolution could be realized. 
       FIG. 15  is an illustration of a progressive magnification pattern  1500  looking down from a vehicle according to certain embodiments of the present invention. Central nadir region  1502  is bounded on its left and right edges by inner left nadir region  1504  and inner right nadir region  1506 , respectively. Inner left nadir region  1504  is bounded on its left edge by outer left nadir region  1508 , while inner right nadir region  1506  is bounded on its right edge by outer right nadir region  1510 . Note that these regions exhibit a minimal degree of overlap and oversampling from one to another. 
       FIG. 16  is an illustration of a progressive magnification pattern  1600  looking down from a vehicle according to certain embodiments of the present invention. Central nadir region  1602  is bounded on its left and right edges by inner left nadir region  1604  and inner right nadir region  1606 , respectively. Inner left nadir region  1604  is bounded on its left edge by outer left nadir region  1608 , while inner right nadir region  1606  is bounded on its right edge by outer right nadir region  1610 . Note that, as above, these regions exhibit a minimal degree of overlap and oversampling from one to another. Within each of the nadir regions  1604  through  1610 , there is a central image region  1614  through  1620  shown shaded in grey. 
       FIG. 17  is an illustration of a progressive magnification pattern  1700  looking down from a vehicle according to certain embodiments of the present invention. In the center of pattern  1700 , a left inner nadir region  1702  and a right inner nadir region  1704  overlap in the center. A left intermediate nadir region  1706  and a right intermediate nadir region  1708  are disposed partly outside of regions  1702  and  1704 , respectively, each sharing an overlapping area with the respective adjacent area by approximately 50%. An outer left nadir region  1710  and an outer right nadir region  1712  are disposed partly outside of regions  1706  and  1708 , respectively, each sharing an overlapping area with the respective adjacent area by approximately 50%. A central image region  1714  is disposed in the center of pattern  1700 , comprised of the central portions of nadir regions  1702  through  1712 . 
       FIG. 18  depicts a schematic of the architecture of a system  1800  according to certain embodiments of the present invention. System  1800  may include one or more GPS satellites  1802  and one or more SATCOM satellites  1804 . One or more GPS location systems  1806  may also be included, operably connected to one or more modules  1808  collecting LIDAR, GPS and/or X, Y, Z location data and feeding such information to one or more data capture system applications  1812 . One or more data capture system applications  1812  may also receive spectral data from a camera array  1822 . A DGPS  1810  may communicate with one or more SATCOM satellites  1804  via a wireless communications link  1826 . One or more SATCOM satellites  1804  may, in turn, communicate with one or more data capture system applications  1812 . 
     One or more data capture system applications  1812  may interface with an autopilot  1816 , an SSD and/or a RealTime StitchG system  1820 , which may also interact with one another. SSD  1814  may be operably connected to RealTime DEM  1818 . Finally, RealTime DEM  1818  and RealTime StitchG  1820  may be connected to a storage device, such as disk array  1824 . 
     The present invention may employ a certain degree of co-mounted, co-registered oversampling to overcome physical pixel resolution limits. These co-mounted, co-registered oversampling techniques work equally well with across track camera arrays or along track camera arrays or any combination thereof.  FIG. 19  is an illustration of a lateral co-mounted, co-registered oversampling configuration  1900  for a single camera array  112  looking down from a vehicle according to certain embodiments of the present invention showing minimal lateral oversampling. The cameras overlap a few degrees in the vertical sidelap area  1904  and  1908 . Whereas  FIG. 19  depicts a 3-camera array, these subpixel calibration techniques work equally well when utilizing any number of camera sensors from 2 to any number of cameras being calibrated. 
     Similar to the imaging sensors in  FIGS. 3 &amp; 4 , the camera sensors may be co-registered to calibrate the physical mount angle offset of each sensor relative to each other and/or to the nadir camera. This provides an initial, “close” calibration. These initial calibration parameters may be entered into an onboard computer system  104  in the system  100 , and updated during flight using oversampling techniques. 
     Referring now to  FIG. 19 , the rectangles labeled A, B, and C represent image areas  1902 ,  1906  and  1910  from a 3-camera array C-B-A (not shown). Images of areas  1902 ,  1906  and  1910  taken by cameras A through C (not shown), respectively, are illustrated from an overhead view. Again, similar to  FIGS. 3 &amp; 4 , because of the “cross-eyed” arrangement, the image of area  1902  is taken by right camera A, the image of area  1906  is taken by center/nadir camera B, and the image of area  1910  is taken by left camera C. Cameras A through C form an array (not shown) that is, in most applications, pointed down vertically. 
     In  FIG. 19 , the hatched areas labeled A/B and B/C sidelaps represent image overlap areas  1904  and  1908 , respectively. The left image overlap area  1904  is where right camera A overlaps with the center/nadir camera B, and the right image overlap area  1908  is where the left camera C overlaps with the center/nadir camera B. In these sidelap areas  1904  and  1908 , the camera sensor grid bisects each pixel in the overlap areas  1904  and  1908 , which effectively quadruples the image resolution in these areas  1904  and  1908  via the mechanism of co-mounted, co-registered oversampling. In effect, the improvement in image/sensor resolution is doubled in each dimension, or 2×2=4 times. This quadrupling of the image resolution also quadruples the alignment precision between adjacent cameras. 
     Further, this quadrupling of alignment precision between adjacent cameras improves the systems  100  alignment precision for all sensors affixed to a rigid mount plate. The cameras and sensors are affixed to a rigid mount unit, which is affixed to the rigid mount plate, as discussed above. In particular, when the angular alignment of adjacent cameras affixed to the rigid mount unit is improved, the angular alignment of the other sensors is also enhanced. This enhancement of alignment precision for the other sensors affixed to the rigid mount plate also improves the image resolution for those sensors. 
     A lateral co-mounted, co-registered oversampling configuration  2000  for two overlapping camera arrays  112  is illustrated in  FIG. 20 . These sub-pixel calibration techniques work equally well with across track camera arrays, along track camera arrays or any combination thereof. In particular,  FIG. 20  is an illustration of a lateral co-mounted, co-registered oversampling configuration  2000  for two overlapping camera arrays  112  looking down from a vehicle according to certain embodiments of the present invention showing maximum lateral oversampling. The adjacent cameras overlap a few degrees in the vertical sidelap areas  2006 ,  2008 ,  2014  and  2016 , and the corresponding cameras overlap completely in the image areas  2002 ,  2010 ,  2018  and  2004 ,  2012 ,  2020 . Whereas  FIG. 20  depicts two 3-camera arrays, these subpixel calibration techniques work equally well when utilizing two overlapping camera arrays with any number of camera sensors from 2 to any number of cameras being calibrated. 
     Similar to the imaging sensors in  FIGS. 3 &amp; 4 , the camera sensors may be co-registered to calibrate the physical mount angle offset of each sensor relative to each other and/or to the nadir camera. In this embodiment, multiple, i.e., at least two, rigid mount units are affixed to a rigid mount plate and are co-registered. This provides an initial, “close” calibration. These initial calibration parameters may be entered into an onboard computer system  104  in the system  100 , and updated during flight. 
     Referring now to  FIG. 20 , the rectangles labeled A, B, and C represent image areas  2002 ,  2010 ,  2018 , and  2004 ,  2012 ,  2020  from two overlapping 3-camera arrays C-B-A (not shown), respectively. Images of areas  2002 ,  2010 ,  2018 , and  2004 ,  2012 ,  2020  taken by cameras A through C (not shown) and overlapping cameras A′ through C′ (not shown), respectively, are illustrated from an overhead view. Again, similar to  FIGS. 3 &amp; 4 , because of the “cross-eyed” arrangement, the image of area  2002  is taken by right camera A, the image of area  2010  is taken by center/nadir camera B, and the image of area  2018  is taken by left camera C. Further, the image of area  2004  is taken by right camera A′, the image of area  2012  is taken by center camera B′, and the image of area  2020  is taken by left camera C′. Cameras A through C and overlapping cameras A′ through C′ form arrays (not shown) that are, in most applications, pointed down vertically. 
     In  FIG. 20 , the hatched areas labeled A/B and B/C sidelaps represent two overlapping image overlap areas  2006 ,  2008  and  2014 ,  2016 , respectively. The left image overlap areas  2006 ,  2008  is where right camera A overlaps with the center/nadir camera B, and where right camera A′ overlaps with the center camera B′, respectively. The right image overlap areas  2014  and  2016  is where the left camera C overlaps with the center/nadir camera B, and where the left camera C′ overlaps with the center camera B′. In these sidelap areas  2006 ,  2008  and  2014 ,  2016 , respectively, the camera sensor grid bisects each pixel in the overlap areas  2006 ,  2008  and  2014 ,  2016 , which effectively quadruples the image resolution in these areas  2006 ,  2008  and  2014 ,  2016  via the mechanism of co-mounted, co-registered oversampling. In effect, the improvement in image/sensor resolution is doubled in each dimension, or 2×2=4 times. This quadrupling of the image resolution quadruples the alignment precision between adjacent cameras, as discussed above. 
     By having two overlapping camera arrays, the image resolution is effectively quadrupled again for the overlapping sidelap overlap areas  2006 ,  2008  and  2014 ,  2016 . This results in an astounding overall 64 times improvement in system  100  calibration and camera alignment. 
     In the overlapping sidelap areas  2006  and  2008 , the overlapping camera sensor grids bisects each pixel in the sidelap areas  2006  and  2008 , which effectively quadruples the image resolution in these areas  2006  and  2008  via the mechanism of co-mounted, co-registered oversampling. Similarly, in the overlapping sidelap areas  2014  and  2016 , the overlapping camera sensor grids bisects each pixel in the sidelap areas  2014  and  2016 , which effectively quadruples the image resolution in these areas  2014  and  2016 . In effect, the improvement in image/sensor resolution is again doubled in each dimension, or 2×2×2×2×2×2=64 times. This overall 64 times improvement of the image resolution also enhances alignment precision by 64 times between adjacent cameras. 
     This 64 times improvement of alignment precision between adjacent and corresponding cameras enhances the systems  100  alignment precision for all sensors affixed to a rigid mount plate. Cameras A through C and, optionally, other sensors are affixed to a first rigid mount unit and cameras A′ through C′ and, optionally, other sensors are affixed to a second rigid mount unit, which are each affixed to a rigid mount plate. In particular, when the angular alignment of adjacent and/or corresponding cameras affixed to the first and/or second rigid mount units is improved, the angular alignment of the other sensors is also enhanced. This enhancement of alignment precision for the other sensors affixed to the rigid mount plate also improves the image resolution for those sensors. 
     By having two overlapping camera arrays, the image resolution is effectively quadrupled for the entire image, not just for the A/B and B/C sidelap overlap areas. Referring now to  FIG. 20 , the overlapping grid detail labeled “OVERLAPPING GRID 4X” represents overlapping areas  2022  and  2024  in right images areas  2018  and  2020 , respectively. In the overlapping areas  2022  and  2024 , the overlapping camera sensor grids bisects each pixel in the overlapping areas  2022  and  2024 , which effectively quadruples the image resolution in these areas  2022  and  2024  via the mechanism of co-mounted, co-registered oversampling. In effect, the improvement in image resolution is doubled in each dimension, or 2×2=4 times. 
     In a preferred embodiment, one camera array is monochrome, and another camera array is red-green-blue. Even though each array covers different color bands, simple image processing techniques are used so that all color bands realize the benefit of this increased resolution. Another advantage provided by these techniques is that, in the case where one camera array is red-green-blue and the other, overlapping camera array is an infrared or near infrared (or some other bandwidth), which results in a superior multi-spectral image. 
     Accordingly, all of the improvements (i.e., 4 times) identified for the embodiment of  FIG. 19  discussed above apply to the embodiment of  FIG. 20 , however, additional significant enhancements (i.e., 64 times) to the systems  100  calibration precision and overall image resolution may be realized through the two overlapping camera arrays. 
       FIG. 21  is an illustration of a fore and lateral co-mounted, co-registered oversampling configuration  2100  for two camera arrays  112  looking down from a vehicle according to certain embodiments of the present invention. In particular,  FIG. 21  is an illustration of a fore and lateral co-mounted, co-registered oversampling configuration  2100  for two overlapping camera arrays  112  looking down from a vehicle according to certain embodiments of the present invention showing minimal fore and minimal lateral oversampling. The adjacent cameras overlap a few degrees in the vertical sidelap areas  2104 ,  2108 ,  2124  and  2128 , and the corresponding cameras overlap a few degrees along the horizontal forelap areas  2112 ,  2116  and  2120 . Whereas  FIG. 21  depicts two 3-camera arrays, these subpixel calibration techniques work equally well when utilizing two overlapping camera arrays with any number of camera sensors from 2 to any number of cameras being calibrated. 
     Similar to the imaging sensors in  FIGS. 3 &amp; 4 , the camera sensors may be co-registered to calibrate the physical mount angle offset of each sensor relative to each other and/or to the nadir camera. In this embodiment, multiple, i.e., at least two, rigid mount units are affixed to a rigid mount plate and are co-registered. This provides an initial, “close” calibration. These initial calibration parameters may be entered into an onboard computer system  104  in the system  100 , and updated during flight. 
     Referring now to  FIG. 21 , the rectangles labeled A, B, and C represent image areas  2102 ,  2106  and  2110  from a 3-camera array C-B-A (not shown), and the rectangles D, E, and F represent image areas  2122 ,  2126  and  2130  from a 3-camera array F-E-D (not shown), respectively. Images of areas  2102 ,  2106  and  2110  taken by cameras A through C (not shown), and images of areas  2122 ,  2126  and  2130  taken by cameras D through F (not shown), respectively, are illustrated from an overhead view. Again, similar to  FIGS. 3 &amp; 4 , because of the “cross-eyed” arrangement, the rear, left image of area  2102  is taken by rear, right camera A, the rear, center image of area  2106  is taken by rear, center/nadir camera B, and the rear, right image of area  2110  is taken by rear, left camera C. Further, the forward, left image of area  2122  is taken by forward, right camera D, the forward, center image of area  2126  is taken by forward, center camera E, and the forward, right image of area  2020  is taken by forward, left camera F. Cameras A through C and overlapping cameras D through F form arrays (not shown) that are, in most applications, pointed down vertically. 
     In  FIG. 21 , the vertical hatched areas represent four image overlap areas  2104 ,  2108 ,  2124  and  2128 . The rear, left image overlap area  2104  is where rear, right camera A overlaps with the center/nadir camera B, and the rear, right image overlap area  2108  is where rear, left camera C overlaps with the center/nadir camera B. The forward, left image overlap area  2124  is where forward, right camera D overlaps with the center/nadir camera E, and the forward, right image overlap area  2128  is where forward, left camera F overlaps with the center camera E. 
     Referring now to  FIG. 21 , the overlapping grid detail labeled “SIDELAP AREA 4:1” represents overlaping sidelap overlap areas  2104 ,  2108  and  2124 ,  2128 . In these sidelap overlap areas  2104 ,  2108 ,  2124  and  2128 , the camera sensor grid bisects each pixel in the overlap areas  2104 ,  2108 ,  2124  and  2128 , which effectively quadruples the image resolution in these areas  2104 ,  2108 ,  2124  and  2128  via the mechanism of co-mounted, co-registered oversampling. In effect, the improvement in image/sensor resolution is doubled in each dimension, or 2×2=4 times. This quadrupling of the image resolution quadruples the alignment precision between adjacent cameras, as discussed above. 
     This quadrupling of alignment precision between adjacent cameras improves the systems  100  alignment precision for all sensors affixed to a rigid mount plate. Cameras A through C and, optionally, other sensors are affixed to a first rigid mount unit and cameras D through F and, optionally, other sensors are affixed to a second rigid mount unit, which are each affixed to a rigid mount plate. In particular, when the angular alignment of adjacent cameras affixed to the first or second rigid mount units is improved, the angular alignment of the other sensors affixed to the mount unit is also enhanced. This enhancement of alignment precision for the other sensors affixed to the rigid mount plate also improves the image resolution for those sensors. 
     Similarly, the horizontal hatched areas represent three image overlap areas  2112 ,  2116  and  2120 . The forward, left image overlap area  2112  is where rear, right camera A overlaps with the forward, right camera D, forward, center image overlap area  2116  is where rear, center/nadir camera B overlaps with the forward, center camera E, and the rear, right image overlap area  2120  is where rear, left camera C overlaps with forward, left camera F. 
     Referring now to  FIG. 21 , the overlapping grid detail labeled “FORELAP AREA 4:1” represents overlaping forelap overlap areas  2112 ,  2116  and  2120 . In these forelap overlap areas  2112 ,  2116  and  2120 , the camera sensor grid bisects each pixel in the overlap areas  2112 ,  2116  and  2120 , which effectively quadruples the image resolution in these areas  2112 ,  2116  and  2120  via the mechanism of co-mounted, co-registered oversampling. In effect, the improvement in image/sensor resolution is doubled in each dimension, or 2×2=4 times. This quadrupling of the image resolution quadruples the alignment precision between corresponding cameras. 
     This quadrupling of alignment precision between corresponding cameras improves the systems  100  alignment precision for all sensors affixed to a rigid mount plate. Cameras A through C and, optionally, other sensors are affixed to a first rigid mount unit and cameras D through F and, optionally, other sensors are affixed to a second rigid mount unit, which are each affixed to a rigid mount plate. In particular, when the angular alignment of corresponding cameras affixed to the first or second rigid mount units is improved, the angular alignment of the other sensors is also enhanced. This enhancement of alignment precision for the other sensors affixed to the rigid mount plate also improves the image resolution for those sensors. 
     Similar to the overlapping sidelap overlap areas  2006 ,  2008  and  2014 ,  2016  in  FIG. 20 , the intersecting forelap and sidelap overlap areas  2114  and  2118  in  FIG. 21  results in an astounding overall 64 times improvement in system calibration and camera alignment. Referring now to  FIG. 21 , the intersecting grid detail labeled “QUAD OVERLAP AREA 64:1” represents intersecting forelap and sidelap overlap area  2118 . In the intersecting forelap and sidelap overlap areas  2114  and  2118 , the overlapping camera sensor grids bisects each pixel in the intersecting areas  2114  and  2118 , which effectively quadruples the image resolution in these areas  2114  and  2118  via the mechanism of co-mounted, co-registered oversampling. In effect, the improvement in image/sensor resolution is again doubled in each dimension, or 2×2×2×2×2×2=64 times. This overall 64 times improvement of the image resolution also enhances alignment precision by 64 times between adjacent cameras. 
     This 64 times improvement of alignment precision between adjacent and corresponding cameras enhances the systems  100  alignment precision for all sensors affixed to a rigid mount plate. Cameras A through C and, optionally, other sensors are affixed to a first rigid mount unit and cameras D through E and, optionally, other sensors are affixed to a second rigid mount unit, which are each affixed to a rigid mount plate. In particular, when the angular alignment of adjacent and/or corresponding cameras affixed to the first and/or second rigid mount units is improved, the angular alignment of the other sensors is also enhanced. This enhancement of alignment precision for the other sensors affixed to the rigid mount plate also improves the image resolution for those sensors. 
     In a preferred embodiment, one camera array is monochrome, and another camera array is red-green-blue. Even though each array covers different color bands, simple image processing techniques are used so that all color bands realize the benefit of this increased resolution. Another advantage provided by these techniques is that, in the case where one camera array is red-green-blue and the other, overlapping camera array is an infrared or near infrared (or some other bandwidth), which results in a superior multi-spectral image. 
     As shown in  FIGS. 19-21 , these techniques may be used to overcome the resolution limits imposed on camera systems due to the inability of optical glass to resolve “very small” objects. As discussed above, these techniques work equally well with across track camera arrays, along track camera arrays or any combination thereof. In particular, there are known physical limits to the ability of optical glass in camera lenses to resolve very small objects. This is often called “the resolving limit of glass”. For example, if 1 millimeter pixels are required from 10,000 feet of altitude, the use of an extremely high magnification telescopic lens would be required to obtain a ground swath of about 100 feet. This is because no matter how many pixels can be produced by a charged-coupled device sensor (e.g., 1 billion pixels), the resolving power of the purest glass would not permit image resolution to 1 millimeter pixels at 10,000 feet of altitude. This example is used to make the point that there are physical limits for pixel resolution in glass as well as pixel density limits for an imaging sensor. 
     The systems  100  imaging sensor alignment in the rigid mount unit(s) affixed to the rigid mount plate and related calibration techniques provide a unique solution to this problem, as described above. By using these techniques, the resolving limitations of glass can effectively be overcome. For example, a single camera array results in 1 times (or no) oversampling benefits. However, two overlapping camera arrays results in 4 times overall improvement in both image resolution and overall geospatial horizontal and vertical accuracy. Further, three overlapping camera arrays results in 16 times overall improvement, four overlapping camera arrays results in 64 times overall improvement, and so on. 
     As can be deduced from these examples, the equation for overall improvement is as follows:
 
overall improvement=4 N  
 
where N is the number of overlapping camera arrays.
 
     If there are four camera arrays, then there are three overlapping camera arrays (i.e., N=3). Accordingly, four camera arrays provide a 64 times (i.e., 4 3 =64 times) overall improvements in both the image resolution and overall geospatial horizontal and vertical accuracy. 
     Further, these subpixel calibration techniques may be combined with the self-locking flight path techniques, as disclosed in U.S. Publication No. 2004/0054488A1, now U.S. Pat. No. 7,212,938 B2, the disclosure of which is hereby incorporated by reference in full. 
     In addition to fore and/or lateral co-mounted, co-registered oversampling as shown in  FIGS. 19-21 , the present invention may also employ flight line oversampling as well to further improve the image resolution, as shown in  FIGS. 13-17 . These flight line oversampling techniques work equally well with across track camera arrays, along track camera arrays or any combination thereof. As shown in  FIGS. 13-17 , the flight lines overlap each other in an image region because each flight line is parallel to one another. These overlapping image regions may be used to calibrate the sensors by along-track and cross-track parallax of images in adjacent flight lines using stereographic techniques. 
     In an embodiment, the self-locking flight path may comprise any pattern that produces at least three substantially parallel travel lines out of a group of three or more travel lines. Further, at least one of the travel lines should be in an opposing direction to the other substantially parallel travel lines. In a preferred embodiment, the travel pattern comprises at least one pair of travel lines in a matching direction and at least one pair of travel lines in an opposing direction. 
     When using the self-locking flight path in opposite directions, the observable positional error may be doubled in some image regions. According, the self-locking flight path technique includes an algorithm to significantly reduce these positional errors. This reduction in positional errors is especially important in the outside, or far left and far right “wing” image areas where the greatest positional errors occur. 
     In an embodiment, these positional improvements may be realized by using a pattern matching technique to automatically match a pixel pattern area obtained from a flight line (e.g., North/South) with the same pixel pattern area obtained from an adjacent flight line (e.g., North/South). In a preferred embodiment, the latitude/longitude coordinates from one or more GPS location systems may be used to accelerate this pattern matching process. 
     Similarly, these subpixel calibration and self-locking flight path techniques may be combined with stereographic techniques because stereographic techniques rely heavily on the positional accuracy of each pixel relative to all other pixels. In particular, these techniques improve the stereographic image resolution and overall geospatial horizontal and vertical accuracy, especially, in the far left and far right “wing” image areas, where the greatest positional errors occur. Further, stereographic techniques are used to match known elevation data with the improved stereographic datasets. Accordingly, the combined subpixel calibration, self-locking flight path and stereographic techniques provide a greatly improved Digital Elevation Model, which results in superior image. 
     Further, these subpixel calibration and self-locking flight path techniques may be used to provide a dynamic, RealTime calibration of the system  100 . In particular, these techniques provide the ability to rapidly “roll on” one or more camera array assemblies  112  onto the system  100 , to immediately begin collecting image data of a target area and to quickly produce high-quality images because the individual sensors have been initially calibrated in the rigid mount unit(s) affixed to the rigid mount plate, as discussed above. In particular, the camera sensors are co-registered to calibrate the physical mount angle offset of each sensor relative to each other and/or to the nadir camera. In an embodiment, multiple, i.e., at least two, rigid mount units are affixed to a rigid mount plate and are co-registered. This provides an initial, “close” calibration. These initial calibration parameters may be entered into an onboard computer system  104  in the system  100 , and updated during flight using oversampling techniques, as discussed above. 
     In an embodiment, the system  100  comprises a RealTime, self-calibrating system to update the calibration parameters. In particular, the onboard computer  104  software comprises a RealTime software “daemon” (i.e., a background closed-loop monitoring software) to constantly monitor and update the calibration parameters using the co-mounted, co-registered oversampling and flight line oversampling techniques, as discussed above. In a preferred embodiment, the RealTime daemon combines subpixel calibration, self-locking flight path and stereographic techniques to improve the stereographic image resolution and overall geospatial horizontal and vertical accuracy. In particular, stereographic techniques are used to match known elevation data to the improved stereographic datasets. Accordingly, the combined subpixel calibration, self-locking flight path and stereographic techniques provide a greatly improved Digital Elevation Model, which results in superior image. 
     In an embodiment, the system  100  comprises a RealTime GPS data system to provide GPS input data. Calibration accuracy is driven by input data from electronic devices such as a GPS and an IMU, and by calibration software which is augmented by industry standard GPS and IMU software systems. Accordingly, a key component of this RealTime, self-calibrating system is a RealTime GPS input data via a potentially low bandwidth communication channel such as satellite phone, cell phone, RF modem, or similar device. Potential sources for the RealTime GPS input data include project controlled ad-hoc stations, fixed broadcast GPS locations (or similar) or inertial navigation via an onboard IMU. 
     In addition to fore and/or lateral co-mounted, co-registered oversampling as shown in  FIGS. 19-21  and the flight line oversampling as shown in  FIGS. 13-17 , the present invention may employ anti-vibration and isothermal methods to further reduce image collection inaccuracies within a single camera and image fusion inaccuracies between two or more camera. For example,  FIGS. 26A and 26B  illustrate an embodiment of multistage isolation of the cameras for increased accuracy. Although  FIGS. 26A and 26B  depict an anti-vibration member and a thermal sleeve, other anti-vibration and isothermal methods may be used. Further, although  FIGS. 26A and 26B  depict a camera array configured in along track, cross-eyed fashion with ortho and oblique imaging sensors, these anti-vibration and isothermal techniques work equally as well with other camera arrays with ortho imaging sensors, oblique imaging sensor or any combination thereof. 
     In the embodiment of the camera array assembly  2600  discussed above, the imaging sensors are arranged in along track, cross-eyed fashion. As depicted in  FIG. 26A , mount unit  2604  comprises a simple structure inside of which imaging sensors  2606 ,  2608 ,  2610  and  2612  are disposed. The imaging sensors  2606  through  2614  are disposed within or along a concave curvilinear array axis  2616  in mount unit  2604  such that the focal axes of all sensors converge and intersect each other within an intersection area bounded by the aperture  2620 . 
     As depicted in  FIG. 26B , oblique imaging sensor  2606  has lens  2628 , ortho imaging sensor  2608  has lens  2630 , ortho imaging sensor  2610  has lens  2632  and oblique imaging sensors has lens  2634 . Vibration of the imaging sensor and lens assembly can cause vibration inaccuracies due to alignment variations of the individual components. Further, thermal expansion and contraction of the imaging sensor and lens assembly can cause thermal inaccuracies due to temperature gradients. To minimize these vibration and thermal inaccuracies, a vibration/thermal sleeve may be placed around each lens and/or a vibration member may be used to secure each lens.  FIGS. 26A and 26B  depict anti-vibration/isothermal sleeves  2622  and  2624  for oblique imaging sensor  2606  and ortho imaging sensor  2608 , respectively. The anti-vibration/isothermal sleeves  2622  and  2624  may be identical or different depending on the specific requirements of the application. The anti-vibration/isothermal sleeves may be made of any material capable of vibrationally dampening and/or thermally isolating the lens. 
     To further minimize vibration inaccuracies, each lens may be secured to an anti-vibration member  2626  as depicted in  FIG. 26A .  FIG. 26B  depicts anti-vibration attachment members  2636  and  2638  for oblique imaging sensor  2606  and ortho imaging sensor  2608 , respectively. The anti-vibration attachment  2636  and  2638  may be identical or different depending on the specific requirements of the application. The anti-vibration attachment member may be made of any material capable of vibrationally dampening and/or thermally isolating the lens. 
     Using the co-mounted, co-registered oversampling techniques, sub-pixel calibration techniques, flight line oversampling techniques, anti-vibration techniques, isothermal techniques or any combination thereto during image collection permits fusing the images obtained from multiple cameras or imaging sensors with the same precision as though the imagery was obtained from a single camera or imaging sensor. In a preferred embodiment, this enhanced metric accuracy creates a virtual frame. 
     The modules, algorithms and processes described above can be implemented in a number of technologies and configurations. Embodiments of the present invention may comprise functional instances of software or hardware, or combinations thereof. Furthermore, the modules and processes of the present invention may be combined together in a single functional instance (e.g., one software program), or may comprise operatively associated separate functional devices (e.g., multiple networked processor/memory blocks). All such implementations are comprehended by the present invention. 
     The embodiments and examples set forth herein are presented to best explain the present invention and its practical application and to thereby enable those skilled in the art to make and utilize the invention. However, those skilled in the art will recognize that the foregoing description and examples have been presented for the purpose of illustration and example only. The description as set forth is not intended to be exhaustive or to limit the invention to the precise form disclosed. Many modifications and variations are possible in light of the above teaching without departing from the spirit and scope of the following claims.