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Proceedings Paper

Feature discovery on segmented objects in SAR imagery using self-organizing neural networks
Author(s): Robert Joseph Fogler; Mark W. Koch; Mary M. Moya; Donald R. Hush
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Paper Abstract

In this paper we investigate the applicability of the feature extraction mechanisms found in the neurophysiology of mammals to the problem of object recognition in synthetic aperture radar imagery. Our approach presents multiple views of target objects to a two-stage-organizing neural network architecture. The first stage, a Neocognitron, performs two layers of feature extraction. The resulting feature vectors are presented to the second stage, an ART-2A classifier self-organizing neural network which clusters the features into multiple object categories. In our first experiments reported in a previous paper, the Neocognitron was trained on raw SAR imagery. The architecture was able to recognize a simulated vehicle at arbitrary azimuthal orientations at a single depression angle while rejecting clutter as well as other vehicles. Feature extraction on raw imagery yielded features that were robust but difficult to interpret. We have performed new experiments in which the self-organization process is used to discover features separately in shadow and bright returns from objects to be recognized. feature extraction on shadow returns yields oriented contrast edge operators suggestive of bipartite simple cells observed in the striate cortex of mammals. Feature extraction on the specularity patterns in bright returns yield a mixture of orientation-independent operators similar to those found in the retina, and a collection of symmetric oriented contrast edge operators. These operators are formed at multiple positions within the receptive fields during the self-organization process and collectively resemble a two-dimensional Haar basis set. we merge the feature operators discovered separately in shadow and bright returns into a combined feature extractor front end. This front end is designed to extract the desired features from raw imagery. We compare the performance of the earlier two-stage neural network with a modified network using the new feature set.

Paper Details

Date Published: 2 September 1993
PDF: 12 pages
Proc. SPIE 1965, Applications of Artificial Neural Networks IV, (2 September 1993); doi: 10.1117/12.152579
Show Author Affiliations
Robert Joseph Fogler, Sandia National Labs. (United States)
Mark W. Koch, Sandia National Labs. (United States)
Mary M. Moya, Sandia National Labs. (United States)
Donald R. Hush, Univ. of New Mexico (United States)

Published in SPIE Proceedings Vol. 1965:
Applications of Artificial Neural Networks IV
Steven K. Rogers, Editor(s)

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