Clay Stewart. “Synthetic Aperture Radar Algorithms.” 2000 CRC Press LLC. <http://www.engnetbase.com>. SyntheticApertureRadar Algorithms ClayStewart ScienceApplicationsInternational Corporation VicLarson ScienceApplicationsInternational Corporation 33.1Introduction 33.2ImageFormation Side-LookingAirborneRadar(SLAR) • UnfocusedSynthetic ApertureRadar • FocusedSyntheticApertureRadar 33.3SARImageEnhancement 33.4AutomaticObjectDetectionandClassificationinSAR Imagery References FurtherReadingandOpenResearchIssues 33.1 Introduction Asyntheticapertureradar(SAR)isaradarsensorthatprovidesazimuthresolutionsuperiortothat achievablewithitsrealbeambysynthesizingalongapertureusingplatformmotion.Thegeometryfor theproductionoftheSARimageisshowninFig.33.1.TheSARisusedtogenerateanelectromagnetic mapofthesurfaceoftheearthfromanairborneorspaceborneplatform.Thiselectromagneticmap ofthesurfacecontainsinformationthatcanbeusedtodistinguishdifferenttypesofobjectsthatmake upthesurface.Thesensoriscalledasyntheticapertureradarbecauseasyntheticapertureisusedto achievethenarrowbeamwidthnecessarytogetahighcross-rangeresolution.InSARimagerythe twodimensionsarerange(perpendiculartothesensor)andcross-range(paralleltothesensor).The rangeresolutionisachievedusingahighbandwidthpulsedwaveform.Thecross-rangeresolution isachievedbymakinguseoftheforwardmotionoftheradarplatformtosynthesizealongaperture givinganarrowbeamwidthandhighcross-rangeresolution.Thepulsereturnscollectedalongthis syntheticaperturearecoherentlycombinedtocreatethehighcross-rangeresolutionimage.ASAR sensorisadvantageouscomparedtoanopticalsensorbecauseitcanoperatedayandnightthrough clouds,fog,andrain,aswellasatverylongranges.Atverylownominaloperatingfrequencies,less than1GHz,theradarevenpenetratesfoliageandcanimageobjectsbelowthetreecanopy.The resolutionofaSARgroundmapisalsonotfundamentallylimitedbytherangefromthesensorto theground.Ifagivenresolutionisdesiredatalongerrange,thesyntheticaperturecansimplybe madelongertoachievethedesiredcross-rangeresolution. ASARimagemaycontain“speckle”orcoherentnoisebecauseitresultsfromcoherentprocessingof thedata.ThisspecklenoiseisacommoncharacteristicofhighfrequencySARimageryandreducing speckle,orbuildingalgorithmsthatminimizespeckle,isamajorpartofprocessingSARimagery beyondtheimageformationstage.Traditionaltechniquesaveragedtheintensityofadjacentpixels, resultinginasmootherbutlowerresolutionimage.AdvancedSARsensorscancollectmultiple polarimetricand/orfrequencychannelswhereeachchannelcontainsuniqueinformationaboutthe c  1999byCRCPressLLC FIGURE 33.1: SAR imaging geometry. surface. Recent systems have also used elevation angle diversity to produce 3-D SAR images using interferometric techniques. In all of these techniques, some sort of averaging is employed to reduce the speckle. The largest consumers of SAR sensors and products are the defense and intelligence communities. These communities use SAR to locate and target relocatable and fixed objects. Manmade objects, especially ones with sharp corners, have very bright signals in SAR imagery, making these objects particularly easy tolocatewith a SAR sensor. A technology similar toSAR isinverse synthetic aperture radar (ISAR) which employs motion of the platform to image the target in cross-range. The ISAR data can be collected from a fixed radar platform since the target motion creates the viewing angle diversity necessary to achieve a given cross-range resolution. ISAR systems have been used to image ships, aircraft, and ground vehicles. Inaddition tothe defense and intelligence applications of SAR, thereareseveral commercial remote sensing applications. Because a SAR sensor can operate day and night and in all weather, it provides the ability to collect data at regular intervals uninterrupted by natural influences. This stable source of ground mapping information is invaluable in tracking agriculture and other natural resources. SAR sensors have also been used to track oil spills (oil-coated water has a different backscatter than natural water), image underground rock formations (at some frequencies the radar will penetrate some soils), track ice conditions in the Arctic, and collect digital terrain elevation data. Radar is an abbreviation for RAdio Detection And Ranging. Radar was developed in the 1930s and 1940s to detect and track ships and aircraft. These surveillance and tracking radars were designed so that a target was contained in a single resolution cell. The size of the resolution cell was a critical design parameter. Smaller resolution cells allowed one to determine the location of a target more accurately and increased the target-to-clutter ratio, improving the ability to detect a target. In the c  1999 by CRC Press LLC 1950s it was observed that one could map the ground (an extended target that takes up more than one resolution cell) by mounting the radar on the side of an aircraft and building a surface map from the radar returns. High range resolution was achieved by using a short pulse or high bandwidth waveform. The cross-range resolution was limited by the size of the antenna, with the cross-range resolution roughly proportional to R/L a where R is the range from the sensor to the ground and L a is the length of the antenna. The physical length of the antenna was constrained, limiting the resolution. In 1951, Carl Wiley of the Goodyear Aircraft Corporation noted that the reflections from two fixed targets in the antenna beam, but at different angular positions relative to the velocity vector of the platform, could be resolved by frequency analysis of the along track (or cross-range) signal spectrum. Wiley simply observed that each target had different Doppler characteristics because of its relative position to the radar platform and that one could exploit the Doppler to separate the targets. The Doppler effect is, of course, the change in frequency of a signal transmitted or received from a moving platform discovered by Christian J. Doppler in 1853: f d = ν/λ where f d is the Doppler shift, ν is the radial velocity between the radar and target, and λ is the radar wavelength. While the Doppler effect had been used in radar processing before the 1950s to separate moving targets from stationary ground clutter, Wiley’s contribution was to discover that with a side looking airborne radar (SLAR), Doppler could be used to improve the cross-range spatial resolution of the radar. Other early work on SAR was done independently of Wiley at the University of Illinois and the University of Michigan during the 1950s. The first demonstration of SAR mapping was done in 1953 by the University of Illinois by performing frequency analysis of data collected by a radar operating at a 3-cm wavelength from a C-46 aircraft. Much work has been accomplished perfecting SAR hardware and processing algorithms since the first demonstration. For a much more detailed description of the history of SAR including the development of focused SAR, phase compensation techniques, calibration techniques, and autofocus, see the recent book by Curlander and McDonough [1]. Before offering a brief description of some processing approaches for forming, enhancing, and interpreting SAR imagery, we give two examples of existing SAR systems and their applications. The firstsystemistheShuttleImagingRadar(SIR)developedbytheNASA JetPropulsionLaboratory (JPL) and flown on several space shuttle missions. This system was designed for non-military collection of geographic data. The second example is the Advanced Detection Technology Sensor (ADTS) built by the Loral Corporation for the MIT Lincoln Laboratory. The ADTS sensor was designed to demonstrate the capability of a SAR to detect and classify military targets. Table 33.1 contains the basic parameters for the ADTS and SIR SAR systems along with details on several other SAR systems. Figure 33.2 shows an example image formed from data collectedby the SIR SAR. The JPL engineers describe this image as follows: ThisisaradarimageofMountRainierinWashingtonstate . Thisimagewasacquired by the Spaceborne Imaging Radar-C and X-band Synthetic Aperture Radar (SIR-C/X- SAR) aboard the space shuttle Endeavor on its 20th orbit on October 1, 1994. The area shown in the image is approximately 59 kilometers by 60 kilometers (36.5 miles by 37 miles). North is toward the top left of the image, which was composed by assigning red and green colors to the L-band, horizontally transmitted and vertically received, and the L-band, horizontally transmitted and vertically received. Blue indicates the C-band, horizontally transmitted and vertically received. In addition tohighlighting topographic slopes facing the space shuttle, SIR-C records rugged areas as brighter and smooth areas as darker. The scene was illuminated by the shuttle’s radar from the northwest so that northwest-facing slopes are brighter and southeast-facing slopes are dark. Forested regions are pale green in color; clear cuts and bare ground are bluish or purple; ice is c  1999 by CRC Press LLC TABLE 33.1 Example SAR Systems Resolution Swath Platform Bands polarization (m) width Interferometry JPL AIRSAR C, L, P–Full 4 10–18 km Cross track L,C Along track L,C SIR-C/X-SAR C, L–Full,X-VV 30 × 30 15–90 Multi-pass ERIM IFSARE X–HH 2.5 × 0.8 10 km Cross track ERIM DCS X–Full < 1 1 km Cross track MIT LL ADTS Ka (33 GHz)–Full 0.33 400 m Multi-pass NORDEN G11 Ku–VV 1,3 5 km 3 Along track 3 Cross track Phase centers SRI UWB 100–300 MHz, 1 × 1 400–600 m None FOLPEN 2 200–400 MHz, 300–500 MHz, HH LORAL UHF 500–800 MHz, 0.6 × 0.6 280 m None MSAR Full NAWC P-3 C, L, X–Full 1.5 × 0.7 5 km Along track X,C NAWC P-3 600 MHz–Full 0.33 × 0.66 930 km None UWB Upgrade tunable over 200– 900 MHz Tier II+ UAV X 1 and 0.3 10 km None SAR dark green and white. The round cone at the center of the image is the 14,435-foot (4,399-meter) active volcano, Mount Rainier. On the lower slopes is a zone of rock ridges and rubble (purple to reddish) above coniferous forests (in yellow/green). The western boundary of MountRainier National Park is seen as a transition from protected, old-growth forest to heavily logged private land, a mosaic of recent clear cuts (bright purple/blue) and partially regrown timber plantations (pale blue). Figure 33.3 is an example image collected by the ADTS system. The ADTS system operates at a nominal frequency of 33 GHz and collects fully polarimetric, 1-ft resolution data. This image was formed using the polarimetric whitening filter (PWF) combination of three polarimetric channels to reduce the speckle noise. The output of the PWF is an estimate of radar backscatter intensity. The image displayed in Fig. 33.3 is based on a false color map which maps low intensity to black followed by green, yellow, and finally white. The color map simply gives the non-color radar sensor output false colors that make the low intensity shadows look black, the grass look green, the trees look yellow, and bright objects look white. This sample image was collected near Stockbridge, New York, and is of a house with an above ground swimming pool and several junked cars in the backyard. The radar is at the top of the image looking down at a 20 ◦ depression angle. The scene contains large areas of grass or crops and some foliage. Note the bright returns from the manmade objects, including the circular above-ground swimming pool, and strong corner reflector scattering from some of the cars in the backyard. Also note the relatively strong return from the foliage canopy. At this frequency the radar does not penetrate the foliage canopy. Note the shadows behind the trees where there is no radar illumination. In this chapter on SAR algorithms, we give a brief introduction to the image formation process in Section 33.2. We review a few simple algorithms for reducing speckle noise in SAR imagery and automatic detection of manmade objects in Section 33.3. We review a few simple automatic object classification algorithms for SAR imagery in Section 33.4. This brief introduction to SAR only contains a few example algorithms. In the Section “Further Reading”, we recommend some starting c  1999 by CRC Press LLC FIGURE 33.2: SAR image of Mt. Rainier in Washington State taken from shuttle imaging radar. points for further reading on SAR algorithms, and discuss several open issues under current research in the SAR community. 33.2 Image Formation In this section, we discuss some basic principles of SAR image formation. For more detailed infor- mation about SAR image formation, the reader is directed to the references given at the end of this chapter. One fundamental scenario under which SAR data is collected is shown in Fig. 33.1.An aircraft flies in a straight path at a constant velocity and collects radar data at a boresight of 90 ◦ .In practice it is impossible for an aircraft to fly in a perfectly straight line at a constant velocity (at least within a wavelength), so motion (phase) compensation of the received radar signal is needed to ac- count for aircraft perturbations. The radar on the aircraft transmits a short pulsed waveform or uses frequency modulation to achieve high range resolution imaging of the surface. The pulses collected from several positions along the trajectory ofthe aircraft are coherently combined to synthesize a long synthetic aperture in order to achieve a high cross-range resolution on the surface. In this section, we first discuss SLAR where only range processing is performed. Next, we discuss unfocused SAR where both range and cross-range processing are executed. Finally, we discuss focused SAR where “focusing” is performed in addition to range and cross-range processing to achieve the highest reso- lution and best image quality. At the end of this section we briefly mention several other important c  1999 by CRC Press LLC FIGURE 33.3: SAR image near Stockbridge, New York, collected by the ADTS. SAR image formation topics such as phase compensation, clutter-lock, autofocus, spotlight SAR, and ISAR. The details of these topics can be found in [1]–[3]. 33.2.1 Side-Looking Airborne Radar (SLAR) SLAR is the earliest radar system for remote surveillance of a surface. These radar systems could only perform range processing to form the 2-D reflectivity map of the surface, so the cross-range resolution is limited by the real antenna beamwidth. These SLAR systems typically operated at high frequencies (microwave or millimeter-wave) to maximize the cross-range resolution. We cover SLAR systems because SLAR performs the same range processing as SAR, and the limitations of a SLAR motivate the need for SAR processing. The resolution of a SLAR system is limited by the radar pulse width in the range dimension, and the beamwidth and slant range in the cross-range dimension: δ r = cT /2cos η δ cr = Rλ/L a where we represent the approximate 3-dB beamwidth of the antenna by λ/L a ,δ r is the range resolu- tion, δ cr is the cross-range resolution, c is the speed of wave propagation, T is the compressed pulse width, η is the angle between the radar beam and the surface, R is the slant range to the surface, λ is the wavelength, and L a is the length of the antenna. The goal is to design the SLAR with a narrow beamwidth, short slant range, and a short pulsewidth to achieve high resolution. In practice, the pulsewidth of the radar is limited by hardware constraints and the amount of “energy on target” required to get sufficient signal-to-noise ratio to obtain a good c  1999 by CRC Press LLC image. To achieve a high range resolution without a short pulse, frequency modulation can be used to synthesize an effectively short pulse. This process of generating a narrow synthetic pulsewidth is called pulse compression. The approach is to introduce a modulation on the transmitted pulse, and then pass the received signal through a filter matched to the transmit signal modulation. The most common transmit waveforms used for pulse compression are linear FM (or chirp) and phase coded. Some radars use a digital version of linear FM called a stepped frequency waveform. We illustrate pulse compression with the ideal application of the linear FM waveform. The square pulse is modulated by a linear FM signal, and the resulting transmit signal is s(t) =    cos  ω 0 † − 1 2 µ † 2  | † |≤T/2 0 | † | >T/2 where the bandwidth (frequency deviation) introduced by the linear FM is f = Tµ/2π If this transmit pulse is perfectly reflected from a stationary point target, range losses are ignored, and we shift in time to remove the two-way delay; the received signal is exactly the same as the transmitted signal. The matched filter response for the transmitted signal is h(t) =  2µ π  1/2 cos  ω 0 † + 1 2 µ † 2  The output of the received signal applied to the matched filter is: ( † ) =  µT 2 2π  1/2 sin ( µT † /2 ) ( µT † /2 ) Re  e j  ω 0 †+ 1 2 µ† 2 +π/4   This output has a mainlobe that has a 4-dB beamwidth of 1/f . The resulting compressed pulse can be significantly narrower than the width of the transmitted pulse with a pulse compression ratio of Tf. The range resolution of the radar has been increased by this pulse compression factor and is now given by: δ r ≈ c/2f cos η Note that the range resolution in the ideal case is now completely independent of the physical width of the transmitted pulse. Performing range compression against real radar targets that Doppler shift the frequency of the receive signal introduces ambiguities resulting in additional signal processing issues that must be addressed. There is a trade-off between the ability of a radar waveform to resolve a target in range and frequency. The performance of a waveform in range-frequency space is given by its ambiguity. The ambiguity function is the output of the matched filter for the signal for which it is matched and for frequency shifted versions of that signal. The references contain a much more detailed description of ambiguity functions and radar waveform design. Using pulse compression, a SLAR system can achieve a very high range resolution on the order of 1 ft or less, but the cross-range resolution of the SLAR is limited by the physical beamwidth of the antenna, the operating frequency, and the slant range. This cross-range resolution limitation of SLAR motivates the use of a synthetic array antenna to increase the cross-range resolution. 33.2.2 Unfocused Synthetic Aperture Radar Figure 33.1 provides a good geometric description of SAR. As with SLAR, the radar platform moves along a straight line collecting radar data fromthe surface. The SAR system goes one step further than c  1999 by CRC Press LLC SLAR by coherently combining pulses collected along the flight path to synthesize a long synthetic array. Thebeamwidth of this synthetic aperture is significantly narrower thanthe physical beamwidth (real beam) of the real antenna. The ideal synthetic beamwidth of this synthetic aperture is θ B = λ/2L θ The factor of two results from the two-way propagation from the moving platform. The unfocused SAR can be implemented by performing FFT processing in the cross-range dimension for the samples in each range bin. This is simply the conventional beamformer for an array antenna. The difference between SAR and real beam radar is that the aperture samples that comprise the SAR are collected at different times by a moving platform. There are several design constraints on a SAR system, including: • The speed of the platform and pulse repetition rate (PRF) of the radar must be mutually selected so that the sample points of the synthetic array are separated by less than λ/2 to avoid grating lobes. • The PRF must be selected so that the swath width is unambiguously sampled. • A point on the ground must be visible to the radar real beam acrossthe entire length of the synthetic array. This limits the size of the real beam antenna. This constraint leads to the observation that with SAR, the smaller the real-beam antenna, the better the resolution, whereas with SLAR the larger the real-beam antenna, the better the resolution. • The SAR assumes that a ground target has an isotropic signal across the collection angle of the radar platform as it flies along the synthetic array. The resolution of the unfocused SAR is limited because the slant range to a scatterer at a fixed location on the surface changes along the synthetic aperture. If we limit the synthetic aperture to a length so that the range from every array point in the aperture to a fixed surface location differs by less than λ/8, then the cross-range resolution of the unfocused SAR is limited to: δ cr =  Rλ/2 33.2.3 Focused Synthetic Aperture Radar The cross-range limitation of an unfocused SAR can be removed by focusing the data, as in optics. The focusing procedure for the SAR involves adjusting the phase of the received signal for every range sample in the image so that all of the points processed in cross-range through the synthetic beamformer appear to be at the same range. The phase error at each range sample used to form the SAR image is φ = 2π λ  d 2 n R  radiar where d n is the cross-range distance from the beam center, R is the slant range to the point on the ground from the beam center, and λ is the wavelength. The range samples can be focused before cross-range processing by removing this phase error from the phase history data. Note that each data point has a different phase correction based on the along-track position of the sensor and the point’s range from the sensor. When focusing is performed, the resulting SAR image resolution is independent of the slant range between the sensor and ground. This can be shown as follows: δ cr = Rθ s c  1999 by CRC Press LLC where, θ s ≈ λ 2L e and L e ≈ Rλ L a therefore, δ cr ≈ L a /2 The effective beamwidth of the synthetic aperture is approximately λ/2L e where the factor of two comes from the two-way propagation of the energy (the exact effective beamwidth depends on the synthetic array taper used to control sidelobes). The length of the effective aperture (L e ) is limited by the fact that a given scatterer on the surface must be in the mainbeam of the real radar beam for every position along the synthetic aperture. The result is that the resolution of the SAR when the data is focused is approximately L a /2. SAR processing can also be developed by considering the Doppler of the radar signal from the surface as first done by Wiley in 1951. When the real beamwidth of the SAR is small, a point on the surface has an approximately linearly decreasing Doppler frequency as it passes through the main beam of the real SAR beamwidth. This time varying Doppler frequency has been shown to be approximately: f d (t) = 2ν 2 |t − t 0 | λR where ν is the velocity of the platform and t 0 is the time that the point scatterer is in the center of the main beam. The change in Doppler frequency as the point passes through the main beam is 2ν 2 T d /λR, and T d isthetimethat the point isinthemainbeam. Aswith linear FMpulse compression, covered in Section 33.2.1, this Doppler signal can be processed through a filter to produce a higher cross-range resolution signal which is limited by the size of the real aperture just as with the synthetic antenna interpretation (δ cr = L a /2). In a modern SAR system, typically both pulse compression (syntheticrange processing)andasyntheticaperture(syntheticcross-rangeprocessing)areemployed. In most cases, these transformations are separable where the range processing is referred to as “fast time” processing and the cross-range processing is referred to as “slow-time” processing. A modern SAR system requires several additional signal processing algorithms to achieve high resolution imagery. In practice, the platform does not fly a straight and level path, so the phase of the raw receive signal must be adjusted to account for aircraft perturbations, a procedure called motion compensation. In addition, since it is difficult to exactly estimate the platform parameters necessary tofocus theSAR image, an autofocusalgorithm isused. Thisalgorithm derivesthe platform parameters from the raw SAR data to focus the imagery. There is also an interpolation algorithm that converts from polar to rectangular formats for the imagery display. Most modern SAR systems form imagery digitally using either an FFT or a bank of matched filters. Typically, a SAR will operate in either a stripmap or spotlight mode. In the stripmap mode, the SAR antenna is typically pointed perpendicular to the flight path (although it may be squinted slightly to one side). A stripmap SAR keeps its antenna position fixed and collects SAR imagery along a swath to one side of the platform. A spotlight SAR can move its antenna to point at a position on the ground for a longer period of time (thus actually achieving cross-range resolutions even greater than the aperture length over two). Many SAR systems support both stripmap and spotlight modes, using the stripmap mode to cover large areas of the surface in a slightly lower resolution mode, and spotlight modes to perform very high resolution imaging of areas of high interest. 33.3 SAR Image Enhancement In this section we review a few techniques for removing speckle noise from SAR imagery. Removing the speckle can make it easier to extract information from SAR imagery and improves the visual quality. c  1999 by CRC Press LLC [...]... below References [1] Curlander, J.C and McDonough, R.N., Synthetic Aperture Radar: Systems and Signal Processing, John Wiley & Sons, New York, 1991 [2] Wehner, D.R., High Resolution Radar, 2nd ed., Artech House, Boston, MA, 1995 [3] Stimson, G.W., Introduction to Airborne Radar, Hughes Aircraft Company, 1983 [4] Skolnik, M., Introduction to Radar Systems, 2nd ed., McGraw-Hill, New York, 1980 [5] Novak,... L., Fractional brownian motion for synthetic aperture radar imagery scene segmentation, Proc IEEE, 81(10), 1511-1522, Oct 1993 [7] Novak, L., Owirka, G., and Netishen, C., Radar target identification using spatial matched filters, Pattern Recognition, 27(4), 607-617, Apr 1994 [8] Stewart, C., Lu, Y.-C., and Larson, V., A neural clustering approach for high resolution radar target classification, Pattern... ,ρ = |H H |2 (33. 1) E (H H · W ∗ ) E |H H |2 · E |W |2 (33. 2) The polarization scattering matrix (using a linear-polarization basis) can then be expressed as 1 0 √ ρ γ 0 ε 0 √ ρ∗ γ 0 γ = σH H | | (33. 3) The pixel intensity (power) is then derived through non-coherent averaging of the power in each of the new polarization components, HV Y = |H H | + √ ε 2 2 + √ W − ρ∗ γ H H γ 1 − |p|2 2 (33. 4) yielding... Transactions on Signal Processing, and IEEE Transactions on Image Processing Conferences IEEE National Radar Conference, IEEE International Radar Conference, and the International Society for Optical Engineering (SPIE) has several SAR Conferences There are numerous open areas of research on SAR signal processing algorithms including: • Still developing an understanding of the utility and applications of multi-polarimetric,... image formation not completely understood • Performance/robustness of different detection, discrimination, and classification algorithms given radar, clutter, and target parameters not completely understood • No fundamental theoretical understanding of performance limitations given radar, clutter, and target parameters (i.e., no Shannon theory) c 1999 by CRC Press LLC ... local estimate of the clutter parameters With these design constraints, a good choice for the CFAR template is just over twice the maximum dimension of the targets of interest FIGURE 33. 5: CFAR template One of these CFAR algorithms, first developed by Goldstein [9], is referred to as the two parameter CFAR or the log-t test: log x − 1 N−1 N i=1 1 N N i=1 log yi log yi − 1 N N i=1 log yi 2 H1 > t < H0... large resolution cell collected by a high frequency radar) the Rayleigh clutter model can be used to represent the speckle under the right statistical assumptions When the number of wavefronts is less than infinity, the K-distribution and other product models do a better job of theoretically and empirically modeling the clutter When the combination of the radar system design and clutter properties results... detection and classification c 1999 by CRC Press LLC algorithms applied to SAR images The PWF does not take into account the effect of the speckle reduction operation on target signals It only minimizes the clutter There has been recent work on polarimetric speckle reduction filters that both reduce the clutter speckle while preserving the target signal Fig 33. 4 shows the three polarimetric channels and the... Trans AES, 9, 84-92, 1972 Further Reading and Open Research Issues A very brief overview of SAR with a few example algorithms is given here The items in the reference list give a more detailed treatment of the topics covered in this chapter SAR is a very active research topic Articles on SAR algorithms are regularly published in many journals and conferences, including: Journals IEEE Transactions on Aerospace... speckle while preserving the target signal Fig 33. 4 shows the three polarimetric channels and the resulting PWF image for an ADTS SAR chip of a target-like object FIGURE 33. 4: Polarimetric processing of SAR data to reduce speckle 33. 4 Automatic Object Detection and Classification in SAR Imagery SAR algorithmic tasks of high interest to the defense and intelligence communities include automatic target . Corporation 33. 1Introduction 33. 2ImageFormation Side-LookingAirborneRadar(SLAR) • UnfocusedSynthetic ApertureRadar • FocusedSyntheticApertureRadar 33. 3SARImageEnhancement. Clay Stewart. Synthetic Aperture Radar Algorithms. ” 2000 CRC Press LLC. <http://www.engnetbase.com>. SyntheticApertureRadar Algorithms ClayStewart