Patent Publication Number: US-7898457-B2

Title: System and method for processing imagery from synthetic aperture systems

Description:
This invention relates to a method of and a system for processing imagery from synthetic aperture radar, sonar, and other systems employing a synthetic aperture. More specifically, it relates to the detection of moving targets in the imagery by observation of shadows produced by such moving targets. 
     Synthetic Aperture Radar (SAR) systems are known. SAR as an imaging technique has been developed to obtain high resolution radar imagery of surface features. It uses a technique of coherently integrating samples collected from a moving platform over a period of time and using the results to create an antenna having a large synthetic aperture to obtain very high azimuth compression of the sampled data. For best results these samples have to be all aligned in phase for a SAR image to be properly focused in azimuth. For a sideways looking SAR in order to obtain a focused image of the static ground this is simply a quadratic phase correction across a nominally straight-line synthetic aperture. 
     Detection of static targets may be performed using pre-screening algorithms as described in Reference  1 . Such algorithms consist of three steps. A bright anomaly detector is applied to the high resolution SAR image followed by a clustering algorithm that groups neighbouring detections. Finally, a simple discriminator is used to reject detection clusters corresponding to false alarms. The pre-screening algorithms can be tuned to detect man-made objects and reject ground clutter. However, an essential pre-requisite is to have fully focused SAR images of the object of interest. 
     For a non-stationary object, there will be errors in the SAR phase history compared to that expected from a static scene. These phase errors distort and de-focus the SAR image and make it significantly more difficult to detect moving targets in SAR imagery. 
     Moving targets can also be detected using Ground Moving Target Indication (GMTI) radars. Such systems use Doppler returns to detect movement, and so will only work when the target has a non-zero radial velocity component toward the radar antenna. 
     Furthermore, the radial velocity has to be more than a specified Minimum Detection Velocity (MDV) to successfully register a detection. Another disadvantage with GMTI is the azimuth location accuracy is considerably worse compared to that possible with SAR, due to the much smaller effective antenna size, and hence directivity, used in GMTI system. 
     For SAR, the problem of focusing of the distorted images caused by moving targets has been investigated by many researchers, as described in References  2 ,  3  and  4 . The basic idea behind all of these approaches is to estimate the phase error based on the direct energy return from the target. The estimated phase is used to either re-focus the SAR image or alternatively utilised in a variety of ways to assist with the detection of moving targets. 
     The drawback of these direct energy techniques is that they can lack robustness to strong clutter, have poor detection performance and can be computationally very intensive. 
     The phenomenon of radar shadows in SAR imagery is well understood for static targets. Any region of the image that is masked from the radar beam by objects within the scene will be in radar shadow. There will be no radar backscatter from this region and the entire shadow region will be at thermal noise level. The shadow region of a target has some useful properties. Since there is no speckle present it tends to be more stable than target and clutter regions. Furthermore, the shape of the shadow is related to the object dimensions. This behaviour associated with static target radar shadows has led to development of techniques that uses the shadow information for target detection (Reference  5 ), for the estimation of building heights (Reference  6 ) and in target classification (Reference  7 ). 
     The object of the present invention is to at least mitigate the problems of the prior art, and to provide an alternative solution for the detection of moving objects in synthetic aperture imagery such as SAR and synthetic aperture sonar. 
     According to a first aspect of the present invention there is provided a method of processing a temporal sequence of returns from a Synthetic Aperture system, the returns comprising a plurality of base images of a region characterised in that it comprises the steps of:
     i) temporally filtering two or more of the plurality of base images to form a reference image, the two or more base images being suitably spatially aligned with respect to each other;   ii) forming a “change detection image” image by normalising the reference image with a given base image from the sequence, the given base image being suitably aligned with the reference image.   

     Note that the synthetic aperture system will typically be a Synthetic Aperture Radar (SAR) system. However, the method of the invention may equally be applied to a synthetic aperture sonar system, or other synthetic aperture systems able to generate images of a region. For convenience the description will, in general, refer to both synthetic aperture radar and sonar systems as SAR systems, and any implementation details given, for example in relation to the figures, that are specific to a particular system will be clear to the normally skilled person. 
     The present invention relates to a new approach that exploits radar or sonar shadow to aid in the detection and location of moving targets. By forming a change detection image as described, any relative movement of objects within the region will show as anomalies within the change detection image. This is because the shadow information caused by a moving object in a single image will be at a lower absolute level when compared to the reference image, as the formation of the reference image dilutes the impact of such shadow information due to the filtering process. 
     This invention extends the idea of using shadow information to the detection of moving targets. The shadow of the target is not reliant on any direct energy return from the target and is therefore free of any SAR image distortion. The shadow can provide a very accurate indication of the true location of the target. However, detecting shadows in individual SAR images will produce false alarms as a result of confusion with shadows from static objects and clutter regions with a low backscatter return. Provided there are available a sequence of SAR images from the same scene each slightly offset in time, then a shadow change detection algorithm will reject false alarms due to non-moving objects. This is because with a sequence of SAR images clutter and static target false alarms will not shift in the imagery. The shadows of moving targets on the other hand will change their position which makes it very easy to distinguish the genuine moving targets from the false alarms. This novel change detection technique has been shown to lead to a large reduction in false alarm rates and makes the detection of moving target shadows practical. 
     Note that in this specification a “base image” is an image of a region that comprises an image generated by a SAR system. Such a base image may be manipulated in terms of its resolution, its orientation or by a spatial filtering process, as appropriate, before being used in steps i) and ii) described above. 
     Preferably the temporally filtered representation of the plurality of base images in step i) is produced by generating an average image by summing successive base images and dividing by the number of base images summed. The base images may be consecutive. A reference image may be produced by other means however, such as by using a maximum filter to select the maximal value for corresponding pixels over a plurality of temporal base images to generate the reference image. The base images should be suitably spatially aligned before being temporally filtered such that, for the sequence of images, any given pixel position in each base image relates to the same part of the region. The alignment process may take place in a prior alignment step, or it may be performed as a part of the filtering process. The alignment may be done by rotating, shifting or otherwise transforming the images relative to each other, or by any other suitable means. Such alignment processes are well known in the relevant art. 
     The normalisation stage of step ii) may be performed by dividing the reference image by the given base image. Alternatively the normalisation may be performed in any other manner commensurate with the filtering process used in step i). 
     Preferably, the change detection image is passed to a threshold detector that highlights those parts of the change detection image that are above a given threshold value to produce an output image. 
     Advantageously, a plurality of change detection images, or, if a threshold detector is used, output images, are produced, each being associated with a particular base image. The reference image used in producing each change detection image is preferably updated for each normalisation step performed. 
     Preferably, each change detection or output image is processed using a pre-screening procedure. The pre-screening may comprise using a Constant False Alarm Rate (CFAR) algorithm. The pre-screening procedure may also comprise clustering or discrimination procedures. 
     Advantageously a temporal tracking algorithm may be performed on the outputs of the pre-screening procedure, or on a sequence of change detection or output images. 
     According to a second aspect of the present invention there is provided a processing system for processing returns from a Synthetic Aperture system comprising a processor adapted to receive data from the synthetic aperture system, the data comprising information from a region taken over a plurality of instances, the processing system comprising a processor adapted to process the data in the form of images, the images comprising a plurality of base images; characterised in that:
         the processor is adapted to apply a temporal filter to two or more of the plurality of base images to form a reference image, the two or more base images being suitably spatially aligned with respect to each other; and   the processor is further adapted to normalise the reference image with a given base image to form an associated change detection image, the given base image being suitably aligned with the reference image.       

     The system may be integrated with a SAR system, or it may alternatively be implemented as a separate computer system. The data from the SAR may comprise “live” data, in which case the processor is preferably adapted to run in real time. Alternatively, the data from the SAR may be stored in a memory, such as on a hard disk, with the processor taking data from the hard disk at a later time. In this case the processor need not be adapted to run in real time. 
     According to a third aspect of the present invention there is provided a computer program adapted to implement the method of the first aspect of the present invention. 
     The computer program may be implemented on a dedicated computer system connected to the SAR, or it may be implemented on a separate computer system. 
    
    
     
       In order that the invention might be more fully understood, embodiments thereof will now be described, by way of example only, with reference to the accompanying drawings, in which: 
         FIG. 1A  is a schematic block diagram of a known synthetic aperture radar data gathering system capable of producing data suitable for the current invention; 
         FIG. 1B  illustrates the SAR imaging geometry configuration deployed by the system of  FIG. 1   a  when collecting data; 
         FIG. 2  is a schematic block diagram of the SAR image formation system of the invention; 
         FIG. 3  is a flow diagram of the shadow detection system processing routines used in the system of  FIG. 2 ; 
         FIG. 4  illustrates schematically the formation of the ratio image by the system of  FIG. 3 ; 
         FIG. 5  illustrates schematically how data points in the SAR image formed by the system of  FIG. 2  are selected for use in the Constant False Alarm Rate (CFAR) algorithm deployed in the system of  FIG. 3 ; 
         FIG. 6  illustrates a binary sub-image for a hypothetical cluster; and 
         FIG. 7  shows, using real SAR imagery, example results of one embodiment of the system described in relation to  FIG. 2 . 
     
    
    
     Referring to  FIGS. 1 , and  2  there is shown a radar target detection system suitable for collecting data for use by the invention, and also for implementation of the invention. It is indicated generally by a data acquisition system  10  and a SAR image formation system  100  respectively. Each of the sub-systems  10  and  100  are explained hence forth. 
     The system  10  referred to in  FIG. 1A  is the helicopter born Airborne Data Acquisition System (ADAS) manufactured and operated by Thales. The system  10  is used in the: collection of raw radar data, prior to processing according to the present invention. It comprises an antenna  12  mounted on the side of the helicopter, a radar  14 , an analogue-to-digital (A to D) converter  16 , a data recorder  18 , a computer  20  and a display device  22 . The computer  20  is under operator control via a mouse  24  and a keyboard  28 . Data passes between parts of the system  10  along data lines  30 , of which those between the computer and A-to-D converter  16 , the computer  20  and data recorder  18 , and the A-to-D converter  16  and data recorder  18  include respective switches  33 ,  34  and  35 . Control lines  36  provide for computer-generated control signals to pass to the radar device  14 , the A-to-D converter  16  and the display device  22 . A communication line  37  is provided for passing messages between the computer  20  and data recorder  18 . 
     The antenna  12  is a high gain horn antenna. The radar  14  is a coherent pulse radar. It uses the same antenna for transmitting and receiving and is therefore a monostatic system. It operates at a centre frequency of 9.75 GHz and at this frequency the antenna has a circular beam width of 7.5 degrees. The radar has a peak transmit power of 200 W that gives it a maximum operating range of 2000 m between itself and the target. It is capable of operating at a bandwidth ranging from 10 MHz to 500 MHz. In collecting the data used in an example embodiment described herein the operating bandwidth was set to 450 MHz. The radar is linearly polarised with both transmit and receive polarisations set to vertical. It operates by transmitting a series of pulses at a Pulse Repetition Frequency (PRF) of 4 kHz. Full details of the ADAS system can be found at the website given in Reference 8. 
     After each pulse has been transmitted, the radar is quiescent for a short duration and then records 1536 values corresponding to radar echo from a series of 1536 concurrent range cells. The range cells are arranged along a radial line extending outwardly from the antenna centre. The first range cell is the nearest in range to the antenna and the last the furthest in range. The radar uses the time delay between the end of transmission of a pulse and the start of the recording of the first range cell to determine the slant range offset to the start of the first range cell. 
     The antenna can pan −30 degrees to +30 degrees in azimuth and from 0 degrees to −20 degrees in elevation. For the collection of data used by the invention as described herein (shown in  FIG. 7 ) the elevation angle is set to −5 degrees. The azimuth angle is adjusted manually so that it is pointing directly at the target. Each of the 1536 values that the radar records for each pulse that is transmitted is a complex valve with a real and imaginary part. The analogue signal is passed through an A-to-D converter where the signal is digitised. All subsequent processing is performed on the digital data. The radar transmits 4000 pulses per second and receives data for 1536 range cells for each pulse. 
     The A-to-D converter  16  is of a standard type, for example a Tektronics model TKAD10C and is capable of digitising both real and imaginary parts of a complex input signal. The data recorder  18  is a standard high-speed magnetic tape recorder, for example an Ampex 107 DCRsi recorder which records data at a rate of 107 Mbits s −1  The computer  20  is a standard personal computer with a Pentium IV processor. The system  10  has a graphical user interface (GUI) which is displayed on the display device  22  and with which an operator may interact with the system  10  using the mouse  24  and the keyboard  28 . Results generated by the system  10  are also displayed on the display device  22 , together with radar housekeeping information generated by radar  14 , such as the operating centre frequency, PRF etc. Referring to  FIG. 1B , for the data collection a 1 km long helicopter track is chosen that gives 645 m slant range offset from the centre of the track (Point O) to the chosen scene centre point on the ground (Point S). The track altitude is chosen to give a grazing angle of −5° from track centre Point O to scene centre Point S. The helicopter is flown in a straight line along the track at a constant speed of 20 m/s. The antenna  12  azimuth is trained on the scene centre point. As the helicopter progresses along the track the azimuth angle of the antenna  12  is adjusted manually to keep the scene centre point within the centre of the radar beam. This form of data acquisition is termed spotlight mode imaging. 
     At the start of the imaging run the helicopter aligns along the designated imaging track. The antenna  12  is pointed at the scene centre point S. The initial azimuth squint angle of the antenna  12  is about +30 degrees. The radar device  14  is switched on using the computer  20 . The switch  35  is closed and raw radar data is recorded onto device  18 . The helicopter flies along the designated 1 km long track at a nominal speed of 20 m/s. As the helicopter progresses along the track the antenna  12  azimuth angle is continually adjusted. As the helicopter travels through the Point O the antenna  12  squint angle falls to 0 degrees. After this point the antenna  12  has negative squint angles. By the time the helicopter reaches the end of the 1 km long track the antenna  12  azimuth angle has squinted to a value of about −30 degrees. The switch  35  is opened and the radar  14  is shut down. The operator notes the address location of the data stored on device  18 . The operator also notes the latitude, longitude and altitude of the chosen scene centre point S. This completes the data acquisition step for system  10 . 
     Shown in  FIG. 2  is a SAR image formation system  100 , used to produce base images for later processing according to the present invention. It comprises a data recorder  180 , a computer  200  and a display device  220 . The computer  200  is under operator control via a mouse  240  and a keyboard  280 . Data passes between parts of the system  100  along data lines  300 . Control lines  360  provide for computer-generated control signals to pass to the display device  220 . A communication line  370  is provided for passing messages between the computer  200  and data recorder  180 . The data recorder  180 , the computer  200  and its peripheries  220  to  370  are identical to the devices numbered  18  through to  37  in  FIG. 1A . 
     In use, an operator uses keyboard  280  to set up parameters required by the system, including the latitude, longitude and altitude of a region of interest, along with required starting and ending azimuth squint angle of antenna  12  and number of squint angle steps. The squint angle is the off-boresight angle. Zero degree squint is the angle pointing in the direction of platform broadside. Positive squint is pointing toward the nose of the platform and negative squint along the tail. Thus for example the squint angle for pointing in the direction of the platform nose is +90 degrees. The operator then finally specifies the range and azimuth resolution and the pixel spacing for the SAR images. The computer  200  then using standard spotlight SAR processing algorithms forms a series of SAR base image files  250 , one for each requested squint angle, from the raw radar pulse data. For the data used in embodiments described herein (see  FIG. 7 ), each radar pulse had 1536 complex digital samples corresponding to 1536 range gates and the radar  14  had recorded the pulses at a rate of 4000 pulses per second. The algorithms for forming spotlight SAR base images at squinted geometry from raw radar pulses are known. References 9 and 10 are just two examples from among several open literature references describing the method for producing spotlight SAR images at arbitrary squint angles from raw radar data from a moving platform. 
     For this example the system  100  is deployed to produce SAR base images from squint angle  15  degrees to 0 degrees. A total of  16  SAR base image files  250  are produced. These SAR base image files will be referred to hence forth as M 1 , M 2  . . . , M 16 . M 1  is the label for the SAR base image file at squint angle 15 degrees, M 2  is the label for the SAR base image file at squint angle 14 degrees and so on. Using this labelling rule M 16  is the label for the SAR base image file at squint angle 0 degrees. 
     Each of the SAR base image files  250  is a 2-dimensional matrix that has 1000 rows with each row containing 284 columns. Each row represents radar data received from a specified range position and each column represents data from specified azimuth position. Therefore, for example element ( 100 ,  12 ) will be the radar data derived from the 100th range cell at the 12th azimuth cell. The full 2-dimensional matrix is thus a SAR image that is 1000 range cell pixels by 284 azimuth cell pixels in size. Each element or pixel in the 2-dimensional matrix is a complex number with a real and imaginary component. The pixel value represents the complex Radar Cross Section (RCS) for the corresponding location on the ground as measured by the radar. For this embodiment the range and azimuth pixel spacing is 0.3 m. Each pixel therefore corresponds to a 0.3 m by 0.3 m radar footprint on the ground. The entire SAR base image file  250  corresponds to a radar footprint that is 300 m long in range and 85.2 m wide in azimuth. The corresponding resolution of the image in range and azimuth is 0.5 m. This ratio between spatial sampling and true image resolution ensures optimum visualisation of the data. SAR base image files  250  are stored on the computer  200 . The data files  250  are stored as binary files with an ASCII header that contains information on the pixel spacing and the SAR image squint angle. Thus for file M 1  the header will state a squint angle of 15 degrees. The generation of the SAR image data files M 1  to M 16  completes the SAR image formation process for system  100 . 
       FIG. 3  shows the steps involved in a first embodiment of the present invention. It shows a flow diagram illustrating the series of routine  1001  to  1008  executed by the computer  200  to process the SAR image files  250  to perform the shadow detection process  1000 . The outcome from these routines is displayed on the display device  220  using the data link  300 . The series comprise an image averaging routine  1001 , an image rotation routine  1002 , a reference image generation routine  1003 , an image ratio generation routine  1004 , another image rotation routine  1005 , a SAR pre-screening routine  1006 , a data transformation routine  1007  and a tracking routine  1008 . 
     For each of image file M i , where i=1, . . . , 16, the image averaging routine  1001  firstly converts the complex values into amplitude values by adding together the square of the real and imaginary component of each pixel value and taking the square root of the sum. The pixel values are then summed together using a 4 by 4 non-overlapping window and divided by the factor 16. This reduces the overall size of the 2-dimensional  1000  by 284 matrix by a factor 4 along each dimension to a matrix of size 250 by 71. The output of routine  1001  is a set of 16 averaged SAR base image files. These are labelled A 1  through to A 16 . Reducing the size of the base image as described above speeds up the subsequent data processing. The averaging of the base image also simplifies the subsequent processing by partially smoothing any noise present in the images. 
     The next step is for the routine  1002  to rotate each of the averaged base image files A i  by the squint angle where the squint angle is obtained from the file ASCII header. Following this rotation the image rows and columns are aligned along the platform across-track and along-track axis. Any given pixel in the transformed images now refers to the same location on the ground. All the images are now aligned with respect to the ground. For this embodiment A 1  is rotated by an angle 15 degrees, A 2  by an angle 14 degrees and so on. The output of the image rotation routine  1002  is a set of 16 rotated averaged SAR base image files. These are labelled B 1  through to B 16 . 
     The reference image generation routine  1003  produces a reference image corresponding to each base image from routine  1002 . For a given image B i  the reference image is formed by adding together the four proceeding images i.e.
 
 B   i   ref   =B   i−1   +B   i−2   +B   i−3   +B   i−4  where  = 5 to 16  (1)
 
     Note that B 1 , B 2 , B 3 , and B 4  do not have a corresponding reference image since they have fewer than four proceeding images. For the current embodiment the routine  1003  produces a total of 12 reference images, labelled as B 5   ref  to B 16   ref . 
     Of course, the reference image may be produced in any other suitable manner. For example, the reference image could comprise an image, each pixel of which represents the maximum pixel value of the corresponding pixel in a plurality of base images. 
     The next step is the normalisation to produce the change detection image. This embodiment produces the change detection image by creating a ratio, and so therefore uses the term “ratio image” to describe the change detection image. The ratio image is generated by the image ratio generation routine  1004  that forms a ratio between the reference and the corresponding image given as
 
 R   i   =B   i   ref   /B   i  where  i= 5 to 16  (2)
 
     Here R i  is the label referring to the ratio image for the i-th sequence image. The ratio image routine  1004  is applied on a total of 12 images starting from sequence number  5  and finishing with image sequence number  16 . 
     The generation of the ratio image (or more generally the change detection image) using the reference image is the key to this invention and is the mechanism by which the change detection for the moving shadows is realised. The mechanism by which this process manipulates the images is further explained with the aid of  FIG. 4 . For the sake of this explanation, each base image is assumed to comprise of a single row of data that has five columns. As a further simplification clutter pixels are assigned a value five and shadow pixels a value one. On the left hand side in  FIG. 4  are shown the image B i    400   a  and immediately above it the four preceding images B i−1  to B i−4    400   b - e . All of these images have a shadow pixel  410  in the left most column which is from a static target. There is also a shadow pixel  420  due to a moving target. It first appears in the image labelled as B i−3    400   d  in the right most column. Over the next few images it shifts toward the right ending up in the column adjacent to the static shadow pixel in image B i    400   a . The four preceding images B − 1   o B 1−4    400   b - e  combine together in summation and averaging step  440  to form the reference image B i   ref    430  which is shown as the centre image in  FIG. 4 . It can be seen that the reference image  430  is preserving the static elements among the images and suppresses those aspects that are changing. Thus the static shadow of column one  410  appears in the reference image but the moving shadow does not. The pixel values in the second to fifth column of B i   ref    430  are the averaged clutter value. The image  450  on the right in  FIG. 4  is then the ratio image R i  formed in the normalisation step by dividing B i   ref  with B i . It can be seen that in the ratio image apart from the second pixel all the others pixel values are one or less. A low value (one that is less than or equal to one) occurs when the scene is not changing. This would occur either when there is static clutter (pixel number three to five of R i ) or when there is static shadow present (pixel number one in R i ). A high value pixel  460  (one that exceeds unity) indicates the presence of a moving shadow. Thus the high value of the second pixel  460  in R i  corresponds to the presence of a moving shadow in B i    420  at the same pixel position. So the ratio image has this very useful property that only pixels with moving shadows will have high value. Thus detecting these high value pixels in the ratio image provides a mechanism for detecting moving shadows and thus moving targets using SAR imagery. 
     Following normalisation in the ratio image routine  1004 , the subsequent routines  1005  to  1006  in the system  1000  of  FIG. 3  describe the method for automatic detection of the moving shadows using the ratio images. 
     For this embodiment, the next step following the creation of the ratio images R i  is to rotate them back into the original image co-ordinate axis. This is performed using the image rotation routine  1005 . As in routine  1002 , the squint angle in the file header determines the amount by which the image is rotated. The difference with routine  1005  is that the rotation is performed in the opposite direction. Thus for example if the squint angle is 10 degrees then the rotation angle will be −10 degrees. The routine processes a total of 12 ratio images and the output files are labelled C i  where i=5 to 16. 
     The pre-screening routine  1006  then processes each of the C i  files and generates a list of plot detections. The pre-screening consists of three sub-routines, initial detection ( 1106 ) (e.g. CFAR processing), clustering ( 1206 ) and discrimination ( 1306 ). 
     The initial detection routine  1106  performs a bright anomaly detection. This applies a CFAR adaptive thresholding to the image C i  to flag up pixels that are anomalously bright compared to the local background. The CFAR detection technique is familiar to those skilled in the art of radar engineering. There are two parts to the calculations of the CFAR threshold. The first involves analysing each pixel of C i  to estimate local background statistics using a CFAR window of data pixels immediately surrounding the test pixel. Thus referring to  FIG. 5  for a given test pixel within an image C i  two rectangular region are identified. An outer rectangle  500  marks the overall extent of the CFAR window and an inner rectangle  510  is a mask window. The CFAR algorithm will use all the pixels within the bounds of the CFAR window excluding the test pixel and the pixels that lie within the mask window, in estimating the background statistics. The background statistics are therefore based on an outer ring of data points  520  shown hatched in  FIG. 5 . The background statistics may range from a simple arithmetic mean of the pixel values to more complex expressions depending upon an assumed statistical model for the background. 
     The second part of the threshold calculation process involves scaling the estimated background statistics by a constant factor. The scaling factor is derived from the Probability of False Alarm (P FA ) value specified for the CFAR detection process. The PFA is fixed for the entire process  1000 . 
     For this embodiment a CFAR window size of 14 rows by 8 columns is chosen. The mask width is set to 4 rows by 12 columns. The P FA  is set to 0.01. The background statistics are assumed to be single look K-distribution. A CFAR algorithm of this form is termed KCFAR, and more details of it can be found in Reference 1. For each image C i  the initial detection routine  1106  outputs a corresponding binary image of the same size. The binary images, labelled D i , where i=5 to 16, comprises of pixels of values zeros and ones where a one denotes a detection and a zero denotes no detection. 
     Clustering routine  1206  searches through the binary image D i  and groups neighbouring pixels with values one into unique clusters. The clustering routine  1206  is known, and more details of generic clustering algorithms of the type used by routine  1206  can be found in Reference 11. For each cluster that is identified in the binary image D i , the clustering routine outputs a list that contains a unique id number for the cluster, the number of pixels in the cluster and the row and column indices of the pixels identified as belonging to that cluster. Thus for example routine  1206  could output a list of the form 
     1 3 100,25; 100,26; 100,27 
     This output list states that routine  1206  has found just one cluster which has been assigned the unique id number “1”. The total number of pixels in this cluster is three. The next three pair of numbers are the row and column indices of the three pixels belonging to cluster “1”. The routine  1206  produces a cluster list file of this format corresponding to each binary image file D i . These cluster list files are ASCII text files and are labelled L i . There is one line of data per unique cluster. A total of 12 L i  files are produced where i=5 to 16. 
     For this embodiment the parameters for the clustering routine  1206  are set to allow a maximum separation of 6 pixels in rows and 2 pixels in columns for any given cluster. The maximum cluster length is set to 100 pixels along the row axis and 50 pixels along the column axis. These values were selected based on expected size of potential shadows. 
     Once the pixels are clustered, a discrimination routine  1306  is used to reject false alarms. It rejects clusters that are too small or too large or those that are not of the correct shape for target shadows. It uses a set of simple shape based features to perform this filtering process. A total of six discrimination features are used. These are the (1) number of pixels in the cluster, (2) the cluster length, (3) the cluster width, (4) the cluster area (length multiplied by width), (5) the cluster aspect ratio (length divided by width) and (6) orientation of the cluster. The discrimination routine  1306  uses the binary image D i  along with the corresponding cluster list file L i  to calculate the six discriminating features associated with each cluster. For a cluster to be accepted as a valid shadow target the following criteria has to be met by the discriminating features:
     Minimum number of pixels=6   Minimum cluster length (m)=6   Maximum cluster length (m)=40   Minimum cluster width (m)=1   Maximum cluster width (m)=15   Minimum cluster area (m 2 )=1   Maximum cluster area (m 2 )=400   Minimum cluster aspect ratio=1   Maximum cluster aspect ratio=15   Orientation angle exclusion range (degrees)=20 to 70   

     The last criterion entails that any cluster whose orientation angle falls within the specified interval is rejected. 
     For each binary image D i , the routine  1306  reads the corresponding cluster list file L i . For each cluster listed in L i , (one per line), routine  1306  extracts a rectangular window from the image D i . The size and location of the extraction window in image D i  is set such that it includes all the pixels identified as belonging to a specific cluster. However, since the cluster pixels are going to be spread somewhat randomly, a rectangle shape sub-image is likely to contain a number of zero-value pixels. 
     This is explained further with the aid of  FIG. 6  which illustrates a 4 by 3 sub-image for a hypothetical five pixel cluster. As can be seen from  FIG. 6  there are seven zero-value pixels in this sub-image. The binary sub-images corresponding to each cluster are used to estimate the cluster length, width and orientation using spatial moments. The spatial moment based estimation method is familiar to those skilled in the art of radar engineering and Reference 12 provides a suitable reference on this subject. From the length and width estimates, the cluster area and aspect ratio values can then be calculated. 
     The discrimination process of routine  1306  involves reading the cluster list L i . For each cluster listed in L i , a rectangle sub-image from the corresponding binary image file D i  is extracted and the six spatial feature are estimated. The feature values are compared with the acceptance criteria listed earlier. Clusters for which any one of the feature value falls outside the acceptance range are reject as false alarms. Details for accepted clusters are saved into a pre-screening detection file. This pre-screening detection file is an ASCII text file and is labelled P. The information that is recorded for each accepted cluster is the image index number “i” , the squint angle corresponding to image i, the cluster centre pixel position as a row and column index number along with the cluster length, width and orientation. Thus the file entry for each accepted cluster will be as follows
     i squint angle cluster_centre_row_no cluster_centre_column no length width orientation   

     The discrimination routine  1306  processes each pair of L i  and D i  data files and store the output in file P using the format shown above. The process is repeated for all 12 pairs of data files and the results from each stored in the file P. 
     The data file P contains the results for the shadow pre-screening as a result of processing all the available images. It contains the complete lists of all the shadows detected over the full set of images. The location for each detection refers to the centre point of the shadow. The next step is for the data transformation routine  1007  to transform the data of file P into a format appropriate for the tracker routine  1008 . 
     Each line in the file P refers to a separate detection. Entries with identical image index number relate to detections reported for the same image. The routine  1007  reads each line and performs two operations. The first operation is to calculate the location of the target using the estimated shadow centre position provided by routine  1006 . The pre-screening routine  1006  provided estimates for the centre position of the shadow region. Secondly, the co-ordinate axis is transformed to align with the platform along-track and across track axis. 
     The original co-ordinate axis is for slant-range to be along the row axis with the column axis representing the direction perpendicular to slant-range. Of course, as the target must lie at an edge of the shadow, in this frame-of-reference the target location is up-range from the shadow centre position. For a given shadow row and column centre position of r s  and c s  respectively the corresponding target row r t  and column c t  positions are estimated approximately as follows
 
r t   =r   s −{length*sin(orientation)+width*cos(orientation)}/2/0.3
 
c t   =c   s  
 
     Here length, width and orientation are the shadow features values in meters and degrees. “sin” and “cos” are the trigonometric functions sine and cosine of an angle and the scaling by the factor 0.3 is due to the row spacing in this embodiment being 0.3 m. 
     The routine  1007  then multiplies the target position values (r t , c t ) by the averaging factor used in routine  1001 . In this embodiment it takes the form of multiplying both r t  and c t  by the factor four. This scales the target position index values back into the original image size which for this embodiment is a 1000 by 284 matrix. 
     Once the target position index has been re-scaled, routine  1007  then rotates the co-ordinates by the squint angle. This aligns the row and column index with the platform cross-track and along track axis. This ensures that detections across all the images are indexed using a common frame-of-reference. This rotation is done because the pre-screening steps of Initial Detection  1106 , Clustering  1206  and Discrimination  1306  as described above were, in this particular embodiment, designed to work on images in their original orientation, whereas the following stage, the particular implementation of tracking routine  1008 , requires in this embodiment the outputs from the pre-screening step  1006  to be suitably aligned. 
     The target row and column positions along with the image index number are recorded to a modified pre-screening file labelled as Q. The file Q is also an ASCII text file that contains one line entry per detection. Each entry is of the form
     i target_row_no target_column_no
 
where the first entry refers to the image index number and the next two entries the position of the target.
   

     Routine  1007  process each target entry in file P and outputs the results to file Q. The file Q has the same number of entries as file P. The data in file Q is in the format that can be passed to the tracker routine  1008 . 
     The tracker routine  1008  applies a standard x-y Kalman filter to the data from file Q. This technique is familiar to those skilled in the art of tracking algorithms. A good reference on Kalman filter trackers is provided by Reference 13. 
     The output from routine  1008  is stored in a tracker output file that is labelled T. This is also an ASCII text file. It contains a list of targets that have produced valid tracks. Each entry recorded as a separate line in file T specifies the image index number, a unique track identifier and the target estimated row position, row velocity (m/s), column position and column velocity (m/s). The tracker can report a number of tracks for a given image. These will have the same image index number but different track id numbers. A valid track is declared only if the target is detected in three or more consecutive images. The entries belonging to a specific track will have the same track id number but incrementing image index numbers. For example an entry in the file T may have the following values
         6 3 145 5.6 55 1.2       

     This entry means that in image  6 , the target belonging to the unique track id  3  is reported at row position  145  with a velocity along the row direction of 5.6 m/s. Its column position is  55  with a velocity along the column axis of 1.2 m/s. The generation of the tracking result file T completes the shadow detection system  1000 . 
     Of course, it will be clear to the normally skilled person that the pre-screening and subsequent tracking of the ratio images may be performed using any suitable algorithm, and the invention is not limited to those methods described herein. 
     To further illustrate the performance of the invention as described herein, reference is now made to  FIG. 7 . This shows the results of the shadow detection system  1000  for one of the SAR base images processed according to the specific embodiment of this invention as described above. The example image selected is image M 10 . This is the tenth image out of the sixteen original SAR base images. The corresponding squint angle is 6 degrees. 
     The image M 10  is processed by the image average routine  1001  and produces the averaged base image A 10    700 . The image orientation is such that slant range is along the rows (vertical axis) and azimuth is along the columns (horizontal axis). Routine  1002  rotates the image by the squint angle and image  701  is the rotated, averaged SAR base image B 10 . The image axes are now aligned with the platform across-track (rows) and along-track (column) axis. Routine  1003  produces the reference image B 10   ref    702  by filtering using the temporal averaging filter based on the average of base images B 9  to B 6 . The output of the normalisation stage, Generate Ratio image, routine  1004 , is the image R 10    703 . The image R 10    703  is then rotated back by the squint angle using routine  1005 . Routine  1006  performs shadow pre-screening as described above and saves the results in output file P. The shadow positions are used to estimate the likely target positions associated with each shadow and these are stored in file Q. Image  704  in  FIG. 7  shows the image B 10  with the detection results from file Q overlaid. Only the detections corresponding to image B 10  are shown. The target positions  705  are marked with circles. The shadow regions  706  as detected by the pre-screening routine  1006  are also shown. These are shown as lines  706 . Image  707  shows the results from the tracker output given in file T Once again the results are only shown for image B 10 . It can be seen that the tracker has reported two targets each marked with a cross  708  that are reported to be moving along the cinder track  709  seen in the SAR image  707 . There has also been one false alarm  710  from the pre-screening stage that the track has rejected. 
     The skilled person will be aware that other embodiments within the scope of the invention may be envisaged, and thus the invention should not be limited to the embodiments as herein described. For example, if used in relation to a sonar system, the synthetic aperture sonar system itself would be different in ways, such as type of sensor, frequencies of operation etc, obvious to the normally skilled person. Images produced by a sonar system may be processed according to the present invention however. 
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