Principles of Modern Radar - Volume 2 1891121537

640 CHAPTER 14 Automatic Target Recognition14.4.2.1 Types of SarFully polarimetric SAR imagery has been shown to improve target-detection performanceas well as the probability of correct classification. Polarimetric data can be optimallycombined using the polarimetric whitening filter to minimize speckle, sharpen objectedges, reduce clutter variance, and enhance target signatures relative to the background(see 14.4.3.2.2) [12]. Principal component analysis (PCA) can also be used to fuse fourpolarizations into one [13]. Using one target type on the GTRI dataset, Mishra and Mulgrewfound that with similar elevation angles the data are less separable with multi-polar imagerythan with single polarization; conversely, with a large span of elevation angles, multi-polarimagery increases the separability of the data [14].Bistatic polarimetric SAR also appears to show promise. Based on limited simulations,its potential has been shown without considerable loss in performance compared withexisting monostatic systems. Increased bandwidth can compensate for degradations inATR performance caused by increasing bistatic angle. Bistatic polarimetric data containsadditional information about the target and possibly its physical features, much like itsmonostatic counterpart [15].Millimeter wave (mmW) SAR is appropriate in some applications that require highresolutionimaging, since new features for discrimination and classification emerge in thisscheme. Objects that are point targets at lower resolutions can be resolved into extendedtargets, and the polarimetric characteristics of individual scattering centers should be morepronounced [16].Ultra-wideband SAR is most helpful when targets are hidden in foliage, as it candetect them by identifying target resonance effects. Fourier and multi-resolution bases,such as the Haar wavelet and Gaussian basis, can be used to extract the spectral informationnecessary to detect wideband resonance effects [17].14.4.2.2 Candidate FeaturesDepending on the application and type of SAR being used, a variety of features may beextracted. These features generally fall into the following categories: polarimetric features,texture features, contrast features, and size features.Polarimetric Features Common polarimetric features used for finding objects that followthe even-bounce nature of some targets of interest, such as tanks and howitzers, include:percent pure (odd and even), percent pure even, and percent bright even [1]. Odd bouncesare defined as |HH + VV| 2 /2, while even bounces are |HH − VV| 2 /2 – |HV| 2 – |VH| 2 ,where HH, VV, HV, and VH are component intensities [18]. A pure odd bounce pixel isone where the intensity of the odd component is greater than 75% of the total intensityand similarly for pure even bounce pixels [18]. Other geometric and polarimetric featuresinclude the range extent and Doppler extent of the 10 strongest VV scatterers [16].( Some)features are based on the normalized Stokes vectors, ⃗S = (S 1 ,S 2 ,S 3 ) =Q1 , U 1 , V where I ,Q,U, and V are the Stokes parameters, and the ellipticity, ε, ofthe1polarization ellipse that describes the angular distance between the equator and the polarizationstate on the Poincaré sphere (graphical tool for visualizing different polarizations).Specific polarimetric features are: the fraction of the 10 strongest VV scatterers wherethe product P HV = ⃗S H⃗S V is < −0.9, and the fraction of the 10 strongest VV scattererswhere |ε H | and |ε V | < 10 ◦ [16].