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5 June 1995 Neural processing of SAR imagery for enhanced target detection
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Neural network models of early visual computation have been adapted for processing single polarization (VV channel) SAR imagery, in order to assess their potential for enhanced target detection. In particular, nonlinear center-surround shunting networks and multi-resolution boundary contour/feature contour system processing has been applied to a spotlight sequence of tactical targets imaged by the Lincoln ADT sensor at 1 ft resolution. We show how neural processing can modify the target and clutter statistics, thereby separating the poplulations more effectively. ROC performance curves indicating detection versus false alarm rate are presented, clearly showing the potential benefits of neural pre-processing of SAR imagery.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Allen M. Waxman, Carol H. Lazott, David A. Fay, Alan N. Gove, and W. R. Steele "Neural processing of SAR imagery for enhanced target detection", Proc. SPIE 2487, Algorithms for Synthetic Aperture Radar Imagery II, (5 June 1995);

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