In this paper, we introduce an efficient end-to-end system for SAR automatic target recognition, giving particular emphasis to the discrimination and classification stages. The target discrimination method, which we present here, is based on the features extracted from the Radon transform. It is used to estimate length and width of the target for discriminating the object as target or clutter. Like the army research laboratory (ARL) and MIT Lincoln Laboratory (MIT/LL) approaches, our classification stage performs gray scale correlation on full resolution sub-image chips. The pattern matching references are constructed by averaging five consecutive spotlight mode images of targets collected at 1-degree azimuth increments. Morphology operations and feature clustering are used to produce accurate image segmentation. The target aspect is estimated to reduce the pose hypothesis search space. Our efficient end-to-end system has been tested using the public target MSTAR database. The system produces high discrimination and classification probabilities with relatively low false alarm rate.
