R. Kothari et D. Ensley, FUNCTION APPROXIMATION FRAMEWORK FOR REGION OF INTEREST DETERMINATIONIN SYNTHETIC-APERTURE RADAR IMAGES, Optical engineering, 37(10), 1998, pp. 2817-2825
Region of interest (ROI) determination is a first and crucial step per
formed in an automatic target recognition (ATR) system. The goal of RO
I determination is to identify candidate regions that may have potenti
al targets. To be most effective, this initial detection (or focus of
attention) stage must reject clutter (noise or countermeasures that pr
ovide target like characteristics), while ensuring that regions with t
rue targets are not missed. We present a novel approach to ROI determi
nation in synthetic aperture radar (SAR) images for ATR based on the p
remise that regions with targets would require a model with more free
parameters to smoothly approximate the magnitude of the return. Toward
that end, we use a sigmoidal multilayered feed-forward neural network
with selected lateral connections between hidden layer neurons to app
roximate the return in disjoint square patches of the SAR image. This
network probably uses as few neurons as possible to produce a desired
approximation and thus enables the determination of the number of para
meters used in approximating the return in an image patch. Those squar
es of :he image that require a large number of neurons (more free para
meters) are then labeled as ROIs. Results obtained with synthetic and
real-world SAR images are used to demonstrate the effectiveness of the
proposed method. A significant advantage of the proposed method is th
at it does not require the presence of a training data set, which, giv
en the variability in SAR images and target signatures, is difficult t
o obtain. (C) 1998 Society of Photo-Optical instrumentation Engineers.
[S0091-3286(98)02310-1].