Patent Publication Number: US-10789467-B1

Title: Polarization-based disturbed earth identification

Description:
RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Application No. 62/678,008, filed on May 30, 2018, entitled “POLARIZATION-BASED DISTURBED EARTH IDENTIFICATION,” the disclosure of which is hereby incorporated herein by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     The embodiments relate generally to identifying an area of disturbed earth and, in particular, to polarization-based disturbed earth identification. 
     BACKGROUND 
     It may be desirable to identify portions of the surface of the earth that have been disturbed relatively recently. This may be particularly helpful, for example, where it is desirable to find items that were recently placed under the surface of the earth. One specific example relates to determining whether hazardous materials, such as improvised explosive devices (IEDs), have been placed under the surface of the earth. Detection of disturbed earth typically involves spectral analysis with an emphasis on spectral bands in the visible-near infrared and in the long-wave infrared, but may also include bands in the short-wave and mid-wave infrared. Disturbed earth is highly varying with several factors: local geology, mineralogy, water content, weathering, roadway use/wear-and-tear, etc. While spectroscopy may work sufficiently well when underlying soil layers, brought to the surface, differ in mineral or organic makeup from that of the adjacent earth, spectroscopy tends to perform poorly when the underlying layers of the earth have the same mineral composition as that of the adjacent earth, and the main difference lies in the mineral granule size distributions between the top undisturbed and the lower disturbed layers. In such situations, spectral analysis alone may not detect an area of disturbed earth. 
     SUMMARY 
     The embodiments implement polarization-based disturbed earth identification. The embodiments utilize electromagnetic radiation (EMR) that has been reflected or emitted from a scene within certain wavebands and that have certain polarization orientations to distinguish between disturbed earth and non-disturbed earth. 
     In one embodiment, a method is disclosed. The method includes receiving, by at least one sensor apparatus comprising a plurality of detector elements, polarized electromagnetic radiation (EMR) in a first waveband from a scene of an area of a surface of the earth and polarized EMR in a second waveband from the scene, the polarized EMR in the first waveband having a first polarization orientation and the polarized EMR in the second waveband having a second polarization orientation. The method further includes outputting, by the at least one sensor apparatus, sensor information that quantifies EMR received by each detector element of the plurality of detector elements. The method further includes processing, by a computing device, the sensor information to generate processed sensor information based at least in part on an amount of polarized EMR received by at least some of the detector elements. The method further includes presenting, on a display device, an image that is based on the processed sensor information. 
     In another embodiment, a system is disclosed. The system includes at least one sensor apparatus that is configured to receive, by the at least one sensor apparatus, polarized electromagnetic radiation (EMR) in a first waveband from a scene of an area of a surface of the earth and polarized EMR in a second waveband from the scene, the polarized EMR in the first waveband having a first polarization orientation and the polarized EMR in the second waveband having a second polarization orientation. The at least one sensor apparatus is further configured to output sensor information that quantifies EMR received by each detector element of the plurality of detector elements. The system includes a processor device communicatively coupled to the at least one sensor apparatus. The processor device is configured to process the sensor information to generate processed sensor information based at least in part on an amount of polarized EMR received by at least some of the detector elements, and present, on a display device, an image that is based on the processed sensor information. 
     Those skilled in the art will appreciate the scope of the disclosure and realize additional aspects thereof after reading the following detailed description of the preferred embodiments in association with the accompanying drawing figures. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee. 
       The accompanying drawing figures incorporated in and forming a part of this specification illustrate several aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure. 
         FIG. 1  is a block diagram of a system according to one embodiment; 
         FIG. 2  is a flowchart of a method for polarization-based disturbed earth identification according to one embodiment; 
         FIG. 3  is a block diagram illustrating filtering aspects of two polarizing filters according to one embodiment; 
         FIGS. 4A-4D  illustrate images of a disturbed earth area of a scene, according to one embodiment; 
         FIGS. 5A-5D  illustrate the same four images illustrated in  FIGS. 4A-4D , with the disturbed earth area outlined; 
         FIG. 6  is a diagram of an image illustrating a disturbed earth area on a display device according to one embodiment; 
         FIG. 7  illustrates additional examples of imagery that may be generated based on the embodiments disclosed herein; 
         FIG. 8  is identical to  FIG. 7  except in grayscale; 
         FIG. 9  is a block diagram of the computing device shown in  FIG. 1  illustrated in additional detail, according to one embodiment; 
         FIG. 10  illustrates an area of a disturbed earth portion of a scene from which electromagnetic radiation (EMR) was received by a first 64×64 detector element portion of a sensor apparatus, and an area of an undisturbed portion of the scene from which EMR was received by a second 64×64 detector element of the sensor apparatus; 
         FIG. 11  illustrates a graph based on the data derived from each of three different images of the scene illustrated in  FIG. 10 , a visible image, a spectral only image, and a spectral-polarimetric image utilizing the polarization-based mechanisms disclosed herein; 
         FIG. 12  illustrates an area of a disturbed earth portion of the scenes illustrated in  FIG. 7  from which EMR was received by a first 64×64 detector element portion of a sensor apparatus, and an area of an undisturbed portion of the scenes illustrated in  FIG. 7  from which EMR was received by a second 64×64 detector element of the sensor apparatus; and 
         FIG. 13  illustrates a graph based on data from each of two different images of the scene illustrated in  FIG. 12 , a visible image, and a spectral-polarimetric image utilizing the polarization-based mechanisms disclosed herein. 
     
    
    
     DETAILED DESCRIPTION 
     The embodiments set forth below represent the necessary information to enable those skilled in the art to practice the embodiments and illustrate the best mode of practicing the embodiments. Upon reading the following description in light of the accompanying drawing figures, those skilled in the art will understand the concepts of the disclosure and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure and the accompanying claims. 
     Any flowcharts discussed herein are necessarily discussed in some sequence for purposes of illustration, but unless otherwise explicitly indicated, the embodiments are not limited to any particular sequence of steps. The use herein of ordinals in conjunction with an element is solely for distinguishing what might otherwise be similar or identical labels, such as “first polarizing filter” and “second polarizing filter,” and does not imply a priority, a type, an importance, or other attribute, unless otherwise stated herein. The term “about” used herein in conjunction with a numeric value means any value that is within a range of ten percent greater than or ten percent less than the numeric value. 
     The phrase “earth” as used herein refers to the earth as well as any celestial body having a surface that comprises soil and may also identify sub-areas of the celestial body including areas of the surface and areas with surface soil and/or soil at depth. The phrase “unpolarized EMR” as used herein refers to electromagnetic radiation (EMR) that contains photons with a random distribution of polarization orientations (i.e., random distribution of electric field orientations). Examples of unpolarized EMR include EMR generated by the sun, flames, and incandescent lamps. “Polarized EMR” refers to a plurality of EMR rays (i.e., photons) that has the same polarization orientation, whether linear or circular. Partially polarized EMR refers to EMR that has an unpolarized EMR component and a polarized EMR component. 
     It may be desirable to identify portions of the surface of the earth that have been disturbed relatively recently. This may be particularly helpful, for example, where it is desirable to find items that were recently placed under the surface of the earth. One specific example relates to determining whether hazardous materials, such as improvised explosive devices (IEDs), have been placed under the surface of the earth. Detection of disturbed earth typically involves spectral analysis with an emphasis on spectral bands in the visible-near infrared and in the long-wave infrared. Disturbed earth is highly varying with several factors: local geology, mineralogy, water content, weathering, roadway use/wear-and-tear, etc. While spectroscopy may work sufficiently well when underlying soil layers, brought to the surface, differ in mineral or organic makeup from that of the adjacent earth, spectroscopy tends to perform poorly when the underlying layers of the earth have the same mineral composition as that of the adjacent earth, and the main difference lies in the mineral granule size distributions between the top undisturbed and the lower disturbed layers. In such situations, spectral analysis alone may not detect an area of disturbed earth. 
     Typically, when the mineral composition of disturbed and undisturbed areas of the earth is chemically similar, the largest differentiation is in the particle size distributions. For most mineral types (not inclusive of organic soils or sands) the disturbed soil has a larger variance in size distribution (i.e., 20-100 μm diameters for iron-rich fine sands) than the undisturbed soil (i.e., 10-30 μm diameters). Aging from rainwater, wind, vehicle traffic and other factors tend to compress the distribution of the soil into a “smoother,” less varied size set of particles through soaking (rain), redistributing finer particles on the top layer (wind) or compressing particles into smooth surfaces (vehicles). The smaller, smoother distribution provides a narrow set of reflected polarization angles with a relatively high degree of polarizability (i.e., specular reflection lobes). One may think of these undisturbed regions as more reflective of forward light (i.e., the sun forward of the observer) and having less back-scatter (due to a smoother, more reflective surface). The larger distribution of particle sizes found in disturbed areas will generally have a wider set of polarization-scatter angles with a relatively lower degree of polarizability (i.e., “rough surface” diffuse reflection). In this sense, the disturbed regions will have more back-scatter reflection and less forward scattered light than the undisturbed areas. “Smoothness” refers to the relative distributed sizes of particles compared with the wavelengths used to analyze the EMR reflected or emitted from the surfaces. Thus, a similar situation applies to EMR that is emitted from surfaces (i.e., thermal emissions from soil). Generally, the smoother the surface, the more polarized the emissions from the surface. The “rougher” the surface, the more unpolarized the emissions from the surface. 
       FIG. 1  is a block diagram of a system  10  according to one embodiment. The system  10  includes a camera  12  that receives EMR  14  from a scene  16  of an area of a surface of the earth. The EMR  14  may comprise EMR in any waveband, including, by way of non-limiting example, long-wave infrared wavebands, mid-wave infrared wavebands, short-wave infrared wavebands, a near-ultraviolet waveband in the ultraviolet light spectrum ranging from about 300 nanometers (nm) to about 400 nm, a visible waveband in a visible light spectrum ranging from about 400 nm to about 700 nm, and a near-infrared (NIR) waveband in an infrared spectrum ranging from about 700 nm to about 1400 nm. The phrase “waveband” includes a range of wavelengths within the relevant spectrum, such as long-, mid-, and short-wave infrared spectrums, within the visible light spectrum, the ultraviolet light spectrum, or the NIR spectrum. Moreover, because soil particle sizes can vary greatly from about 1 micron (e.g., “moondust”) to as large as 1 millimeter (large sand granules), the wavelengths useful for discriminating soils can vary greatly as well, in some embodiments from about 100 nanometers to about 100 microns in wavelength. 
     The system  10  includes a filter assembly  18  that is positioned in the camera  12  in an optical path  20  along which the EMR  14  travels. In one embodiment, the filter assembly  18  includes a first polarizing filter  22 - 1  positioned in the optical path  20  that is configured to receive the EMR  14  from the scene  16  and to pass a first subset of EMR  14  comprising EMR  14  in a first waveband that has a first polarization orientation and EMR  14  in one or more second wavebands irrespective of polarization orientation. The first polarizing filter  22 - 1  blocks the EMR  14  in the first waveband that does not have the first polarization orientation. The first polarizing filter  22 - 1  may also pass the EMR  14  in a number of other wavebands irrespective of polarization orientation. As an example, the first polarizing filter  22 - 1  may pass a red waveband comprising the EMR  14  having wavelengths in a range between about 550 nm and 750 nm (referred to herein as “red EMR  14 ” for the sake of brevity) that has a particular polarization orientation, such as a vertical polarization orientation, and block red EMR  14  that has any other polarization orientation. The first polarizing filter  22 - 1  may pass all other EMR  14  having wavelengths in a range between about 300 nm and 500 nm irrespective of polarization orientation and EMR  14  having wavelengths in a range between about 800 nm and 1000 nm (or greater) irrespective of polarization orientation. In some embodiments the polarizing filters discussed herein may comprise, by way of non-limiting example, an absorptive-based polarizer using wire-grids, polarizing beam-splitters, birefringent optical materials, and the like. 
     The filter assembly  18  includes a second polarizing filter  22 - 2  positioned in the optical path  20  downstream of the first polarizing filter  22 - 1 . The second polarizing filter  22 - 2  is configured to receive the first subset of the EMR  14  from the first polarizing filter  22 - 1  and to pass a second subset of the EMR  14  including the EMR  14  in the second waveband that has a second polarization orientation and the EMR  14  in the first waveband that has the first polarization orientation. The second polarizing filter  22 - 2  blocks the EMR  14  in the second waveband that does not have the second polarization orientation. 
     As an example, the second polarizing filter  22 - 2  may pass a blue waveband comprising the EMR  14  having wavelengths in a range between about 300 nm and 500 nm (referred to herein as “blue EMR  14 ” for the sake of brevity) that has a particular polarization orientation, such as a horizontal polarization orientation, and block blue EMR  14  that has any other polarization orientation. The first polarizing filter  22 - 2  may pass all other EMR  14  having wavelengths in a range between about 500 nm and 1000 nm (or greater) irrespective of polarization orientation (including the vertically polarized red EMR  14  passed by the first polarizing filter  22 - 1 ). 
     The first polarizing filter  22 - 1  and the second polarizing filter  22 - 2  may be referred to generally herein singularly as “polarizing filter  22 ” or in the plural as “polarizing filters  22 .” The phrase “pass” as used herein in conjunction with the polarizing filter  22  refers to the transmission by the polarizing filter  22  of the EMR  14  that has passed through the polarizing filter  22 , and does not imply active electronics that actively transmit the EMR  14 . 
     The second subset of the EMR  14  thus, in this example, comprises horizontally polarized blue EMR  14 , vertically polarized red EMR  14 , and includes other wavebands of any polarization orientation. The second subset of the EMR  14  may pass through one or more lens arrangements  24  and then impinge upon a sensor apparatus  26  in the camera  12 . The sensor apparatus  26  comprises a plurality of detector elements  28  (typically arranged in a matrix structure) sensitive to the EMR  14  in all desired wavebands, including the EMR  14  in the first waveband and the EMR  14  in the second waveband, which, in this example, are in the visible wavebands of blue EMR  14  and red EMR  14 . Thus, in the example of the EMR  14  in the visible light spectrum, the sensor apparatus  26  may comprise, for example, a complimentary metal-oxide semiconductor (CMOS) or a charge-coupled device (CCD) sensor device. In other embodiments, such as embodiments that operate in the infrared (IR) wavebands, the sensor apparatus  26  may comprise either a CMOS or CCD sensor, or a focal plane array (FPA), depending on the particular IR wavebands of interest. The sensor apparatus  26  is also configured to spectrally distinguish the EMR  14  in the first waveband from the EMR  14  in the second waveband. For example, the sensor apparatus  26  may include, or may be downstream of, a color filter array such as a Bayer filter mosaic, or other color separating mechanisms, such as spectral dispersive elements (e.g., gratings, prisms, and the like), notch filters, bandpass filters, and the like. The sensor apparatus  26  may have any desired resolution of detector elements  28 , such as a 1024×1024 grid of 1,048,576 detector elements  28 . 
     The sensor apparatus  26  generates sensor information  30  that characterizes the EMR  14  impinging on the detector elements. In particular, in some embodiments, the sensor information  30  comprises detector element information  32  for each detector element  28  that quantifies the amount of EMR received by each respective detector element  28 . The detector element information  32  may include a red value (RV) that quantifies an intensity of red EMR that impinged upon a respective detector element  28 , a green value (GV) that quantifies an intensity of green EMR that impinged upon the respective detector element  28 , and a blue value (BV) that quantifies an intensity of blue EMR that impinged upon the respective detector element  28 . Collectively, the green values contained in the detector element information  32  is sometimes referred to as the “green channel,” the red values contained in the detector element information  32  is sometimes referred to as the “red channel,” and the blue values contained in the detector element information  32  is sometimes referred to as the “blue channel.” 
     The system  10  includes a computing device  34  that has a processor device  36  coupled to a memory  38  and a display device  40 . The computing device  34  may have additional components, such as a storage device, and other hardware elements of a computing device that are not shown for purposes of simplicity. While the computing device  34  may be external to the camera  12 , in other embodiments the computing device  34  may be integrated into the camera  12 . 
     The system  10  may also include an objective lens  42  positioned in the camera  12  to receive the EMR  14  from the scene  16 . The EMR  14  may travel downstream along the optical path  20  to the filter assembly  18 . Note that the filter assembly  18  may be placed at any location along the optical path  20 , including at a pupil plane or an image plane. In some embodiments, a number of different filter assemblies  18  may exist, depending on an expected type of soil, and the filter assemblies  18  may be constructed with a frame that can be detachably inserted into a transparent holder  44 . In this manner, a particular filter assembly  18  that operates in two (or more) desired wavebands with desired polarization orientations may be inserted into the transparent holder  44 , depending on the particular composition of the soil in the scene  16 . 
     In some embodiments, the first polarizing filter  22 - 1  is fixed with respect to the second polarizing filter  22 - 2 . In some embodiments, the first polarizing filter  22 - 1  may be coupled directly to the second polarizing filter  22 - 2  via an adhesive, an optical coating, or the like. In other embodiments, the first polarizing filter  22 - 1  may be movable with respect to the second polarizing filter  22 - 2 . In some embodiments, the polarization orientation of the first polarizing filter  22 - 1  may be movable with respect to the polarization orientation of the second polarizing filter  22 - 2 . For example, the polarization orientation of the first polarizing filter  22 - 1  may be movable between a range of 20 degrees to 90 degrees with respect to the polarization orientation of the second polarizing filter  22 - 2 , or within any other desired range. The first polarizing filter  22 - 1  and the second polarizing filter  22 - 2  may be sourced from any of a number of optics providers and manufacturers, such as CODIXX AG located at Steinfeldstrabe 3, 39179 Barleben, Germany; Edmund Optics Inc., located at 101 East Gloucester Pike, Barrington, N.J. 08007-1380; Deposition Sciences, Inc., located at 3300 Coffey Lane, Santa Rosa Calif. 95403; or MOXTEK, Inc., located at 452 W 1260 N, Orem, Utah 84057. 
     While for purposes of illustration the polarization orientations are discussed herein as being linear, the embodiments are not limited to linear polarization orientations, and have applicability to circular and elliptical polarization orientations as well. Thus, the first polarization orientation may be a right-hand circular polarization orientation and the second polarization orientation may be a left-hand circular polarization orientation. 
     While the embodiments may utilize a number of different wavebands, in some additional embodiments, the first polarizing filter  22 - 1  may pass a blue waveband comprising the EMR  14  having wavelengths in a range between about 370 nm and 570 nm (referred to herein as “the first EMR  14 ” for the sake of brevity) that has a particular polarization orientation, such as a vertical polarization orientation, and block the first EMR  14  that has any other polarization orientation. The first polarizing filter  22 - 1  may pass all other EMR  14  having wavelengths in a range between about 600 nm and 1000 nm irrespective of polarization orientation. The second polarizing filter  22 - 2  may pass a waveband comprising the EMR  14  having wavelengths in a range between about 650 nm and 1000 nm (referred to herein as “the second EMR  14 ” for the sake of brevity) that has a particular polarization orientation, such as a horizontal polarization orientation, and block the second EMR  14  that has any other polarization orientation. The second polarizing filter  22 - 2  may pass all other EMR  14  having wavelengths in a range between about 300 nm and 600 nm and between about 900 nm and 1000 nm irrespective of polarization orientation. 
     In yet another embodiment, the first polarizing filter  22 - 1  may pass a waveband comprising the EMR  14  having wavelengths in a range between about 550 nm and 750 nm (referred to herein as “the first EMR  14 ” for the sake of brevity) that has a particular polarization orientation, such as a vertical polarization orientation, and block the first EMR  14  that has any other polarization orientation. The first polarizing filter  22 - 1  may pass all other EMR  14  having wavelengths in a range between about 300 nm and 500 nm and between about 800 nm and 1000 nm irrespective of polarization orientation. The second polarizing filter  22 - 2  may pass a waveband comprising the EMR  14  having wavelengths in a range between about 370 nm and 570 nm (referred to herein as “the second EMR  14 ” for the sake of brevity) that has a particular polarization orientation, such as a horizontal polarization orientation, and block the second EMR  14  that has any other polarization orientation. The second polarizing filter  22 - 2  may pass all other EMR  14  having wavelengths in a range between about 600 nm and 1000 nm irrespective of polarization orientation. 
     In another embodiment, the first EMR  14  waveband is in a range of about 700 nm to about 1000 nm, and the second EMR  14  waveband is in a range of about 1100 to about 2500 nm. In another embodiment, the first EMR  14  waveband is in a range of about 1100 nm to about 2500 nm, and the second EMR  14  waveband is in a near-infrared waveband in a range of about 3000 nm to about 4000 nm. In another embodiment, the first EMR  14  waveband is in a range of about 4000 nm to about 5000 nm, and the second EMR  14  waveband is in a range of about 8000 nm to about 14,000 nm. 
     The camera  12  receives partially polarized EMR  14  from the scene  16 . The EMR  14  is partially polarized because it includes unpolarized EMR and polarized EMR after reflecting off or emitting from the area of the surface of the light within a field of view of the camera  12 . The EMR  14  travels along the optical path  20  to the first polarizing filter  22 - 1 , which passes to the second polarizing filter  22 - 1  a first subset of EMR  14  comprising EMR  14  in a first waveband that has a first polarization orientation and EMR  14  in one or more second wavebands irrespective of polarization orientation. The first polarizing filter  22 - 1  blocks the EMR  14  in the first waveband that does not have the first polarization orientation. As an example, the first polarizing filter  22 - 1  may pass red EMR  14  that has a particular polarization orientation, such as a vertical polarization orientation, and block red EMR  14  that has any other polarization orientation. The first polarizing filter  22 - 1  may pass all other EMR  14  having wavelengths in a range between about 300 nm and 500 nm irrespective of polarization orientation and EMR  14  having wavelengths in a range between about 800 nm and 1000 nm irrespective of polarization orientation. 
     The second polarizing filter  22 - 2  is configured to receive the first subset of the EMR  14  from the first polarizing filter  22 - 1  and passes a second subset of the EMR  14  including the EMR  14  in the second waveband that has a second polarization orientation and the EMR  14  in the first waveband that has the first polarization orientation. The second polarizing filter  22 - 2  blocks the EMR  14  in the second waveband that does not have the second polarization orientation. As an example, the second polarizing filter  22 - 2  may pass blue EMR  14  that has a particular polarization orientation, such as a horizontal polarization orientation, and block blue EMR  14  that has any other polarization orientation. The second polarizing filter  22 - 2  may pass all other EMR  14  having wavelengths in a range between about 500 nm and 1000 nm irrespective of polarization orientation (including the vertically polarized red EMR  14  passed by the first polarizing filter  22 - 1 ). 
     The sensor apparatus  26  receives the EMR  14  transmitted by the second polarizing filter  22 - 2 , and outputs the sensor information  30  that quantifies the EMR  14  received by each detector element  28 . In one embodiment a sensor information processing function  46  processes the sensor information  30  based on an amount of polarized EMR  14  received by each detector element  28  to generate processed sensor information  48 . Such processing may visually distinguish disturbed earth areas from immediately adjacent earth areas via a visual characteristic, such as color, contrast, intensity, or the like. The sensor information processing function  46  is discussed in greater detail below. The display device  40  may then present an image  50  that is based on the processed sensor information  48 , and that visually depicts disturbed areas of the earth that may be invisible or barely visible to the naked eye. In some embodiments, as illustrated in  FIG. 1 , the image  50  may include indicia, such as text and/or an outline about the disturbed earth area to more quickly draw a human eye to the disturbed earth area of the image  50 . 
       FIG. 2  is a flowchart of a method for polarization-based disturbed earth identification according to one embodiment.  FIG. 2  will be discussed in conjunction with  FIG. 1 . The sensor apparatus  26  receives the polarized EMR  14  in the first waveband from the scene  16  and the polarized EMR  14  in the second waveband from the scene  16 . The polarized EMR  14  may be reflected and/or emitted from the scene  16 . The EMR  14  in the first waveband has a first polarization orientation and the EMR  14  in the second waveband has a second polarization orientation ( FIG. 2 , block  1000 ). Note, as discussed above, the sensor apparatus  26  may also receive unpolarized EMR  14  in other wavebands. The sensor apparatus outputs the sensor information  30  that quantifies the EMR  14  received by each detector element  28  ( FIG. 2 , block  1002 ). The computing device  34  processes the sensor information  30  to generate the processed sensor information  48  based at least in part on an amount of polarized EMR  14  received by each detector element  28  ( FIG. 2 , block  1004 ). The computing device  34  presents the image  50  that is based on the processed sensor information  48  on the display device  40  ( FIG. 2 , block  1006 ). 
       FIG. 3  is a block diagram illustrating filtering aspects of the two polarizing filters  22 - 1 ,  22 - 2  according to one embodiment. The EMR  14  will be described herein as partially polarized, meaning that some of the EMR  14  has been polarized by reflections or emissions off surfaces from the scene  16 , and some of the EMR  14  is unpolarized. Merely for purposes of illustration, the first polarization orientation will be a zero degree orientation  52 - 1 , and the second polarization orientation will be a ninety degree orientation  52 - 2 . The EMR  14  includes EMR  14 A that comprises the EMR  14  in the first waveband, both polarized and unpolarized, and EMR  14 B that includes the EMR  14  in the second waveband, both polarized and unpolarized. The EMR  14  also includes EMR  14   OTHER  in other wavebands irrespective of polarization. 
     The first polarizing filter  22 - 1  passes the EMR  14 B and the EMR  14   OTHER  downstream, and blocks the EMR  14 A UNPOL  comprising the EMR  14  in the first waveband having any polarization orientation other than the first polarization orientation. The first polarizing filter  22 - 1  passes downstream the EMR  14 A POL  comprising the EMR  14  in the first waveband having the first polarization orientation. The second polarizing filter  22 - 2  passes the EMR  14 A POL  and the EMR  14   OTHER  downstream, and blocks the EMR  14 B UNPOL  comprising the EMR  14  in the second waveband having any polarization orientation other than the second polarization orientation. The second polarizing filter  22 - 2  passes downstream the EMR  14 B POL  comprising the EMR  14  in the second waveband having the second polarization orientation. The EMR  14 A POL , EMR  14 B POL , and the EMR  14   OTHER  in wavebands other than the first waveband and the second waveband are received by the sensor apparatus  26 . 
     While for purposes of illustration the two polarization orientations  52 - 1 ,  52 - 2  have been described as vertical and horizontal, the embodiments are not limited to vertical polarization orientations and horizontal polarization orientations, and may comprise any two polarization orientations that differ from one another by sufficient angles, such as 45 degrees, 60 degrees, or 90 degrees. Additionally, polarization orientations may include circular or elliptical polarizations and may include any phase angle differences between the orthogonal components of the transverse electromagnetic fields. 
       FIGS. 4A-4D  illustrate images of a disturbed earth area of the scene  16 , according to one embodiment.  FIGS. 4A and 4B  depict the scene  16  as seen by an unaided human eye. Note that the scene  16  includes a pavement area  54 , a grass area  56 , and a soil area  58 . Within the soil area  58  is a disturbed earth area invisible, or practically invisible, to the unaided human eye.  FIG. 4C  illustrates the scene  16  using spectral analysis only, and without the use of the polarizing filters  22 - 1 ,  22 - 2 .  FIG. 4D  illustrates the scene  16  in accordance with aspects of the present embodiments, including both spectral and polarization processing.  FIG. 4D  illustrates an image generated based on the system  10  illustrated in  FIG. 1 .  FIG. 4D  visually depicts a disturbed earth area  60  contained within the soil area  58 . 
       FIGS. 5A-5D  illustrate the same four images illustrated in  FIGS. 4A-4D , with the disturbed earth area outlined. 
       FIG. 6  illustrates the image  50  illustrating a disturbed earth area on the display device  40  according to one embodiment. In this embodiment the processor device  36  identifies, in the image  50 , a sub-area of the area of the earth as a disturbed earth sub-area. In one embodiment, the processor device  36  identifies the sub-area of the area of the earth as the disturbed earth sub-area by determining a contrast difference in image pixels of the image  50 , and based on the contrast difference between image pixels that depict the sub-area and surrounding image pixels adjacent to the image pixels that depict the sub-area, identifies the sub-area as the disturbed earth sub-area. In general, the mean and variance distributions of values which are highly different between disturbed and undisturbed indicate high separability. 
       FIG. 7  illustrates additional examples of imagery that may be generated based on the embodiments disclosed herein. In this example a scene  62  includes a disturbed earth area  64  in a soil area  66 . The disturbed earth area  64  is visually indistinguishable from the surrounding soil area  66  to the naked eye. The system  10  processes the EMR  14  from the scene  62  in the manner discussed above with regard to  FIG. 1  and generates the processed sensor information  48  based on an amount of polarized EMR  14  received by the detector elements  28 . The processor device  36 , in one embodiment, may generate an image  68  that visually distinguishes the disturbed earth area  64  from surrounding soil  66  via colorization. 
     In another embodiment, the processor device  36  may utilize one or more scene classification algorithms on the processed sensor information  48  to identify the disturbed soil from other scene backgrounds such as undisturbed soil, vegetation, rocks, debris, and other environment elements. The processor device  36  may then present, on the display device  40 , a classification image  70  based on the processed sensor information  48 . The classification image  70  may be presented with one or more tags identifying the different classified sub-areas, such as disturbed soil, undisturbed soil, vegetation, rocks, debris, and the like. 
       FIG. 8  is identical to  FIG. 7  except in grayscale. 
       FIG. 9  is a block diagram of the computing device  34  in additional detail, according to one embodiment. The computing device  34  may comprise any computing or electronic device capable of including firmware, hardware, and/or executing software instructions to implement the functionality described herein, such as a desktop computing device, a laptop computing device, a smartphone, a computing tablet, or the like. The computing device  34  includes the processor device  36 , a memory  38 , and a system bus  72 . The system bus  72  provides an interface for system components including, but not limited to, the memory  38  and the processor device  36 . The processor device  36  can be any commercially available or proprietary processor. 
     The system bus  72  may be any of several types of bus structures that may further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and/or a local bus using any of a variety of commercially available bus architectures. The memory  38  may include non-volatile memory  74  (e.g., read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.), and volatile memory  76  (e.g., random-access memory (RAM)). A basic input/output system (BIOS)  75  may be stored in the non-volatile memory  74  and can include the basic routines that help to transfer information between elements within the computing device  34 . The volatile memory  76  may also include a high-speed RAM, such as static RAM, for caching data. 
     The computing device  34  may further include or be coupled to a non-transitory computer-readable storage medium such as a storage device  80 , which may comprise, for example, an internal or external hard disk drive (HDD) (e.g., enhanced integrated drive electronics (EIDE) or serial advanced technology attachment (SATA)), HDD (e.g., EIDE or SATA) for storage, flash memory, or the like. The storage device  80  and other drives associated with computer-readable media and computer-usable media may provide non-volatile storage of data, data structures, computer-executable instructions, and the like. Although the description of computer-readable media above refers to an HDD, it should be appreciated that other types of media that are readable by a computer, such as Zip disks, magnetic cassettes, flash memory cards, cartridges, and the like, may also be used in the operating environment, and, further, that any such media may contain computer-executable instructions for performing novel methods of the disclosed examples. 
     A number of modules can be stored in the storage device  80  and in the volatile memory  76 , including an operating system and one or more program modules, such as the sensor information processing function  46 , which may implement the functionality described herein in whole or in part. 
     All or a portion of the examples may be implemented as a computer program product  82  stored on a transitory or non-transitory computer-usable or computer-readable storage medium, such as the storage device  80 , which includes complex programming instructions, such as complex computer-readable program code, to cause the processor device  36  to carry out the steps described herein. Thus, the computer-readable program code can comprise software instructions for implementing the functionality of the examples described herein when executed on the processor device  36 . The processor device  36 , in conjunction with the program modules, may serve as a controller, or control system, for the computing device  34  that is to implement the functionality described herein. The computing device  34  may also include a communications interface  84  suitable for communicating with a network as appropriate or desired. 
     Mechanisms for processing the sensor information  30  to generate the processed sensor information  48  are discussed herein. In general, in some embodiments, the sensor information processing function  46  reduces the correlation in the levels of output channels of the sensor apparatus  26  (or in products of the channels) either as an autocorrelation or a cross-correlation within the channels data, yet produces a result with similar spatial context and optimally stretched channel data for maximum contrast of signals with little variance. Moreover, the sensor information processing function  46  may orthogonalize the channels data and produce the equivalent of a matched linear filter to reduce the autocorrelation of a signal as far as possible. In some embodiments, this is achieved by normalizing the power spectrum of the channels, and orthogonalizing their contributions in terms of their corresponding noise levels, sometimes referred to as noise whitening. 
     Orientation of the polarization axis can be set to either color (e.g., blue or red for example) at 0-degrees polarization orientation and the other at 90-degrees polarization orientation for optimal S1-type calculation across the entire spectrum. In some embodiments, with the sun facing the observation, the most blue polarizer set at 90-degrees will reduce the polarization reflection from undisturbed areas and transmit the diffuse scatter of the disturbed area in the most red 0-degree polarizer. 
     Summary of the System  10  According to Some Embodiments 
     A readout of the sensor information  30 , such as the red, green, blue (RGB) values in the detector element information  32 , corresponds to both the spectral and polarimetric levels of the scene (soil) radiance. Examples of the processing that the sensor information processing function  46  may implement to provide maximum contrast is given in two general methods, both of which can be optimized in calibration to a given spectral response of the sensor apparatus  26  and to the typical soil types expected. Generally, the phrase “contrast” is used to refer to statistical separation between the disturbed and undisturbed areas. 
     Simple processing may use the following steps: 
     1) Calibration of the sensor apparatus  26  to broad-band, unpolarized visible and near-infrared (VNIR) light sources, evenly illuminating on a flat diffusely reflecting surface (e.g., a calibration sphere or a Spectralon white board, for example). 
     2) Normalization of the response of the sensor apparatus  26  to provide uniform and unbiased (equal) response in the entire spectrum responsivity. 
     3) Calculation of the spectral S1 from scene (soil) radiance, with normalized channels, can be determined by the following formula:
 
 S 1=ABS(90 degree channel−0 degree channel)
 
     4) Further normalization can be done with the following formula:
 
 S 1/SUM( R,G,B  channels)
 
     5) Interpretation of the processed imagery will depend on the calibration, normalization and the soil type expected. Often the disturbed areas will appear either more red or more blue than the undisturbed areas, depending on choices of filter orientation and processing. 
     Extensive processing may include steps  1  and  2  discussed above, and may include the following inputs to a decorrelation processing (discussed below). The following combinations of inputs are some of the possibilities (not exclusive of others) of three-channel imagery using the following combinations: 
     1) Red, Green Blue with their respective visible and near infrared responses through the first polarizing filter  22 - 1  and the second polarizing filter  22 - 2 . 
     2) Two color channels, plus the S1 computed in step  3  above. 
     3) S1, SUM(R,G,B) as computed in the steps above, and one color channel (trained a priori). 
     The three channel imagery combinations can be used in a decorrelation process, described below. 
     Decorrelation Stretching Matrix Calculation and Application 
     The algorithm uses large numbers of scene data values and context statistical calculations to separate spectral-polarimetric information, depending on the combination/selection of three channels described above. Most scene radiance will have a high background spectral-polarimetric overlap as well as spectral overlap/pollution in the sensor RGB filter transmissions. This is alleviated by decorrelation/orthogonalization in the following manner: 
     Selected 3-channel orthogonalization is done by finding the eigenfunctions of the three channels and transforming the data to the eigenspace (orthogonal space). The eigenspace is computed from the covariance of the scene pixels in each of the color channels. Using the sampled pixels, nine sums are needed to calculate the covariance matrix for the three channels. These sums are: 
     For l=1,3 (three channels); m=1,1, and sampling n pixels, 
     
       
         
           
             
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     where Pk,l is the value of the kth pixel for Channel l. The covariance matrix is computed, using the following formulas: 
     
       
         
           
             
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     The eigenvectors and eigenvalues of the system described by the covariance matrix are computed. The matrix of eigenvectors is referred to as the rotation matrix, R, in subsequent steps. The “stretching vector” (or normalization vector), s, is formed by taking the reciprocal of the square root of each element in the eigenvalue vector, and multiplying it by the desired standard deviation for the output image channels. For true normalization, the desired standard deviation would be one, but in order to yield output values in the appropriate range for eight bit pixels (i.e., byte data) a higher target value is used. The final transformation matrix, T, is composed from the rotation matrix and the stretching vector. This is done by the following matrix multiplication:
 
 T=R   †   sR  
 
wherein R is a matrix composed of the eigenvectors computed from the covariance matrix. s is the stretching vector, which is a vector of the eigenvalues from Cov(l,m). The matrix multiplication results in a transformation matrix, T, which is used on the original image to transform it into a contrast stretched image that emphasizes the target of interest.
 
     Prior to doing the transformation upon the image, this transformation is applied to a vector of the means of the input channels. The result is used to compute the offsets needed to reposition the output image values to the 0 to 255 dynamic range of 8-bit data. For each pixel in the scene, the output pixel vector (3 valued) is computed by applying the final transformation matrix, and then the offset vector. 
       FIG. 10  illustrates an area  100  of a disturbed earth portion of a scene  16  from which EMR was received by a first 64×64 detector element portion of a sensor apparatus, and an area  102  of an undisturbed portion of the scene from which EMR was received by a second 64×64 detector element of the sensor apparatus. Table 1 identifies computed means and standard deviations of grayscale sensor data associated with the areas  100  and  102  using three different images, a visible image (e.g.,  FIG. 5A ), a spectral only image (e.g.,  FIG. 5C ), and a spectral-polarimetric image utilizing the polarization-based mechanisms disclosed herein (e.g.,  FIG. 5D ). 
     
       
         
           
               
               
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 AREA 
                 MEAN 
                 STD. DEVIATION 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 Visible Undisturbed (area 102) 
                 219.5962 
                 25.5328 
               
               
                 Visible Disturbed (area 100) 
                 217.6638 
                 13.6955 
               
               
                 Spectral Undisturbed (area 102) 
                 142.6045 
                 31.3900 
               
               
                 Spectral Disturbed (area 100) 
                 174.5908 
                 20.1813 
               
               
                 Spectral-Polarimetric Und. (area 102) 
                 156.0469 
                 43.2468 
               
               
                 Spectral-Polarimetric Dis. (area 100) 
                 85.3577 
                 24.5746 
               
               
                   
               
            
           
         
       
     
       FIG. 11  illustrates a graph based on the data from Table 1, for each of three different images (referred to as “sensor type” in  FIG. 11 ): a visible image (e.g.,  FIG. 5A ), a spectral only image (e.g.,  FIG. 5C ), and a spectral-polarimetric image utilizing the polarization-based mechanisms disclosed herein (e.g.,  FIG. 5D ). Along the Y-axis are means and error bars associated with standard deviations. On the left side of each image type (i.e., sensor type) is plotted the undisturbed area statistics. On the right side of each image type (i.e., sensor type) is plotted the disturbed area statistics. A box bridging between the overlapping variance/deviations between the disturbed area  100  and the undisturbed area  102  is drawn to show the significance of separability in each image type (i.e., sensor type). The more similar the means and the higher the variance overlap, the more difficult to separate the disturbed area  100  from the undisturbed area  102 . 
     The statistical overlap of the variance (standard deviation) between disturbed earth portions and undisturbed portions is highest in the visible image, lower in the spectral-only image, and nearly zero in the spectral-polarimetric image. The clean distinction in statistics of the spectral-polarimetric data indicates high separation of the two classes of soil. 
       FIG. 12  illustrates an area  104  of a disturbed earth portion of the scenes  62 ,  68  illustrated in  FIG. 7  from which EMR was received by a first 64×64 detector element portion of a sensor apparatus, and an area  106  of an undisturbed portion of the scenes  62 ,  68  from which EMR was received by a second 64×64 detector element of the sensor apparatus. Table 2 identifies computed means and standard deviations associated with the areas  104  and  106  using two different images, a visible image (scene  62 ) and a spectral-polarimetric image (scene  68 ) utilizing the polarization-based mechanisms disclosed herein. 
     
       
         
           
               
               
               
             
               
                 TABLE 2 
               
               
                   
               
               
                 AREA 
                 MEAN 
                 STD DEVIATION 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 Visible Undisturbed (area 106) 
                 209.3135 
                 12.0476 
               
               
                 Visible Disturbed (area 104) 
                 206.8928 
                 3.8739 
               
               
                 Spectral-Polarimetric Und. (area 106) 
                 208.2217 
                 8.6739 
               
               
                 Spectral-Polarimetric Dis. (area 104) 
                 193.8867 
                 4.4354 
               
               
                   
               
            
           
         
       
     
       FIG. 13  illustrates a graph based on the data from Table 2, for each of the two different images (referred to as “sensor type” in  FIG. 13 ), a visible image (e.g., scene  62 ), and a spectral-polarimetric image (scene  68 ) utilizing the polarization-based mechanisms disclosed herein. Along the Y-axis are means and error bars associated with standard deviations. On the left side of each image type (i.e., sensor type) is plotted the undisturbed area statistics. On the right side of each image type (i.e., sensor type) is plotted the disturbed area statistics. A box bridging between the overlapping variance/deviations between the disturbed area  104  and the undisturbed area  106  is drawn to show the significance of separability in each image type (i.e., sensor type). 
     The statistical overlap of the variance (std-dev) between disturbed and undisturbed is highest in the visible, and zero in the spectral-polarimetric. The clean distinction in statistics of the spectral-polarimetric data indicates high separation of the two classes of soil. 
     Those skilled in the art will recognize improvements and modifications to the preferred embodiments of the disclosure. All such improvements and modifications are considered within the scope of the concepts disclosed herein and the claims that follow.