Principles of Modern Radar - Volume 2 1891121537

14.4 Synthetic Aperture Radar 651they assume the observation parameter distributions approximate a mixture of Gaussianor autoregressive densities closely [59].14.4.4.16 Support Vector MachineSupport vector machines (SVMs) have been shown to have strong classification, faulttolerance, and generalization abilities [61]. SVM is a margin-based classifier that uses asubset of training samples to find support vectors giving the maximum decision boundaryhyperplane of the classifier [62, 41]. For an SVM to discriminate between more than twoclasses, a parallel, cascading, or hierarchical decision scheme is generally employed [63].SVMs cannot select an optimal feature subset from the complete extracted feature set; anoptimal feature subset simplifies classification and can improve classification performance[61]. By combining an SVM with a rough set, or a rough estimate of the feature setconsisting of lower and upper approximations, the classification phase of ATR can beperformed well [61].14.4.4.17 Correlation FilterCorrelation filters are used in the classification stage of SAR ATR because they are shiftinvariant, offering varying degrees of distortion tolerance, and are not tailored to a particularsensor or image properties [64]. Other advantages of advanced correlation techniquesinclude: filter synthesis can be performed off-line; image preprocessing is unnecessarybecause of the filter’s distortion tolerance and robustness; the processing complexity dependson the scene size but not its content; design optimization and performance boundingis relatively easy; and the algorithm maps well into hardware [64]. We discuss only a fewof the many examples of these filters below.14.4.4.18 Maximum Average Correlation HeightThe maximum average correlation height (MACH) filter is an attractive correlation filteroption because of its better distortion tolerance than other alternatives; however, it haspoor clutter rejection performance because it relies strongly on the average training image.Variations of the extended MACH filter can be used to improve the clutter rejection performance[65]. The MACH filter generates peaks from which a peak-to-sidelobe ratio (PSR) iscalculated; the test image belongs to the class with the highest PSR [64]. A MACH filter canbe used in conjunction with a distance classifier correlation filter (DCCF) to improve performanceby applying the DCCF if the MACH PSR is above an acceptable threshold [66].14.4.4.19 Distance Classifier Correlation FilterThe distance classifier correlation filter (DCCF) classifies targets using the distance ofthe test image correlation to the class average correlation, instead of only the outputcorrelation peak, and has shown better classification results than other correlation filters[66]. For best performance, interclass distance should be maximized and the averagecorrelation energy minimized, while the average intraclass distance, represented by theaverage similarity measure, should be minimized [66]. The DCCF involves processingby a transformation filter to maximize the average spectral separation between classesand, thus, enhance discrimination; then, the shift-invariant minimum mean square erroris calculated to determine the distance between the transformed test image and eachtransformed reference image [66]. The image is classified according to the class of thereference image yielding the smallest distance [66].