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Proceedings Paper

Automatic recognition of SAR targets using directional filter banks and higher-order neural networks
Author(s): Sang-Il Park; Mark J. T. Smith; Russell M. Mersereau
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Paper Abstract

This paper presents a new approach for the classification of SAR targets that combines maximally decimated directional filter banks with higher-order neural networks (HONNs). HONNs are neural networks that permit the input signals to be multiplied together in addition to the more common operations such as weighting, summing, and pointwise nonlinearities of typical neural nets. HONNs have long been proposed as image classifiers whose performance can be made invariant to geometric transformations of the input imagery by using a method for decreasing dimensionality such as coarse coding. Most past image classifiers using HONNs have been tuned for carefully thresholded binary images, which generally cannot be derived from low-contrast imagery such as SAR without a significant loss of information. As an alternative, we use a novel HONN implementation that accepts gray-level input pixels using directional filter banks. In order to do this, a new modified tree-structured directional filter bank structure is proposed in this paper, where each of the subbands has directional visual information from a given input. The performance of the proposed approach is demonstrated with imagery taken from the public MSTAR database.

Paper Details

Date Published: 24 August 1999
PDF: 8 pages
Proc. SPIE 3718, Automatic Target Recognition IX, (24 August 1999); doi: 10.1117/12.359942
Show Author Affiliations
Sang-Il Park, Georgia Institute of Technology (United States)
Mark J. T. Smith, Georgia Institute of Technology (United States)
Russell M. Mersereau, Georgia Institute of Technology (United States)

Published in SPIE Proceedings Vol. 3718:
Automatic Target Recognition IX
Firooz A. Sadjadi, Editor(s)

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