Processing of synthetic aperture radar images by the boundary contour system and feature contour system

An improved boundary contour system (BCS) and feature contour system (FCS) neural network model of preattentive vision was applied to two large images containing range data gathered by a synthetic aperture radar sensor. Early processing by shunting center-surround networks compresses signal dynamic range and performs local contrast enhancement. Subsequent processing by filters sensitive to oriented contrast, including short-range competition and long-range cooperation, segments the image into regions. Finally, a diffusive filling-in operation within the segmented regions produces coherent visible structures. The combination of BCS and FCS capitalizes on the form-sensitive operations of a neural network model to detect and enhance structure based on information over large, variably sized and variably shaped regions of the image