Recognition of Occluded Objects in SAR Images

Recognition of occluded objects in synthetic aperture radar SAR images is a signi cant problem for automatic target recognition Stochastic models provide some attractive features for pattern matching and recognition under partial occlusion and noise In this paper we present a discrete hidden Markov modeling HMM based approach for recognizing objects in synthetic aperture radar SAR images We identify the peculiar characteristics of SAR sen sors and using these characteristics we develop feature based multiple models for a given SAR image of an object The models exploiting the relative geometry of feature locations or the amplitude of SAR radar return are based on sequentialization of scattering centers extracted from SAR images In order to improve performance we integrate these models synergistically using their probabilistic estimates for recognition of a particular target at a speci c azimuth Experimental results are presented using both synthetic and real SAR images