Synthetic aperture radar processing by a multiple scale neural system for boundary and surface representation

A neural network model of boundary segmentation and surface representation is developed to process images containing range data gathered by a synthetic aperture radar (SAR) sensor. The boundary and surface processing are accomplished by an improved Boundary Countour System (BCS) and Feature Countour System (FCS), respectively, that have been derived from analyses of perceptual and neurobiological data. BCS/FCS processing makes structures such as motor vehicles, roads, and buildings more salient and interpretable to human observers than they are in the original imagery. Early processing by ON cells and OFF cells embedded in shunting center-surround network models preprocessing by lateral geniculate nucleus (LGN). Such preprocessing compensates for illumination gradients, normalizes input dynamic range, and extracts local ratio contrasts. ON cell and OFF cell outputs are combined in the BCS to define oriental filters that model corticla simple cells. Pooling ON and OFF outputs at simple cells overcomes complementary processing deficiencies of each cell type along concave and convex contours, and enhances simpl;e cell sensitivity to image edges. Oriented filter outputs are rectified and outputs sensitive to opposite contrast polarities are pooled to define complex cells. The complex cells output to stages of short-range spatial competition (or endstopping) and orientational competition among hypercomplex cells. Hypercomplex cells activate long-range cooperative bipole cells that begin to group image boundaries. Nonlinear feedback between bipole cells and hypercomplex cell segments image regions by cooperatively completing and regularizing the most favored boundaries while suppressing image noise and weaker boundary groupings. Boundary segmentation is performed by three copies of the BCS at small, medium, and large filter scales, whose subsequent interaction distances covary with the size of the filter. Filling-in of multiple surface representations occurs within the FCS at each scale via a boundary-gated diffusion process. Diffusion is activated by the normalized LGN ON and OFF outputs within ON and OFFfilling-in domains. Diffusion is restricted to the regions defined by gating signals from the corresponding BCS boundary segmentation. The filled-in opponent ON and OFFsignals are subtracted to form double opponent surface representations. These surface representations are shown by any of three methods to be sensitive to both image ratio contrasts and background luminance. The three scales of surface representation are then added to yield a final multiple-scale output. The BCS and FCS are shown to perform favorably in comparison to several other techniques for speckle removal.