Physics-based classification of targets in SAR imagery using subaperture sequences
It is well known that radar scattering from an illuminated object is often highly aspect dependent. We have developed a multi-aspect target classification technique for SAR imagery that incorporates matching-pursuits feature extraction from each of a sequence of subaperture images, in conjunction with a hidden Markov model that explicitly incorporates the target-sensor motion represented by the image sequence. This approach exploits the aspect dependence of the signal features to facilitate maximum-likelihood identification. We consider SAR imagery containing targets concealed by foliage.