Analysis of multiple-view Bayesian classification for SAR ATR

Classification of targets in high-resolution synthetic aperture radar imagery is a challenging problem in practice, due to extended operating conditions such as obscuration, articulation, varied configurations and a host of camouflage, concealment and deception tactics. Due to radar cross-section variability, the ability to discriminate between targets also varies greatly with target aspect. Potential space-borne and air-borne sensor systems may eventually be exploited to provide products to the warfighter at tactically relevant timelines. With such potential systems in place, multiple views of a given target area may be available to support targeting. In this paper, we examine the aspect dependence of SAR target classification and develop a Bayesian classification approach that exploits multiple incoherent views of a target. We further examine several practical issues in the design of such a classifier and consider sensitivities and their implications for sensor planning. Experimental results indicating the benefits of aspect diversity for improving performance under extended operating conditions are shown using publicly released I-foot SAR data from DARPA's MSTAR program.