Use of model order for detecting potential target locations in SAR images

The Region of Interest (ROI) detection stage of an Automatic Target Recognition System serves the crucial role of identifying candidate regions which may have potential targets. The large variability in clutter (noise or countermeasures which provide target like characteristics) complicate the task of developing accurate ROI determination algorithms. Presented in this paper is a new paradigm for ROI determination based on the premise that disjoint local approximation of the regions of a SAR image can provide discriminatory information for clutter identification. Specifically, regions containing targets are more likely to require complex approximators (i.e. ones with more free parameters of a higher model order). We show preliminary simulations results with two different approximators (sigmoidal multi-layered neural networks with lateral connections, and radial basis function neural networks with a model selection criterion), both of which attempt to produce a smooth approximation of disjoint local patches of the SAR image with as few parameters as possible. Those patches of the image which require a higher model order are then labeled as ROIs. Our preliminary results show that sigmoidal networks provide a more consistent estimate of the model order than their radial basis function counterparts.