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Proceedings Paper

Detection and classification of MSTAR objects via morphological shared-weight neural networks
Author(s): Nipon Theera-Umpon; Mohamed A. Khabou; Paul D. Gader; James M. Keller; Hongchi Shi; Hongzheng Li
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Paper Abstract

In this paper we describe the application of morphological shared-weight neural networks to the problems of classification and detection of vehicles in synthetic aperture radar (SAR). Classification experiments were carried out with SAR images of T72 tanks and armored personnel carriers. A correct classification rate of more than 98% was achieved on a testing data set. Detection experiments were carried out with T72 tanks embedded in SAR images of clutter scenes. A near perfect detection rate and a low false alarm rate were achieved. The data used in the experiments was the standard training and testing MSTAR data set collected by Sandia National Laboratory.

Paper Details

Date Published: 15 September 1998
PDF: 11 pages
Proc. SPIE 3370, Algorithms for Synthetic Aperture Radar Imagery V, (15 September 1998); doi: 10.1117/12.321856
Show Author Affiliations
Nipon Theera-Umpon, Univ. of Missouri/Columbia (Thailand)
Mohamed A. Khabou, Univ. of Missouri/Columbia (United States)
Paul D. Gader, Univ. of Missouri/Columbia (United States)
James M. Keller, Univ. of Missouri/Columbia (United States)
Hongchi Shi, Univ. of Missouri/Columbia (United States)
Hongzheng Li, Univ. of Missouri/Columbia (United States)

Published in SPIE Proceedings Vol. 3370:
Algorithms for Synthetic Aperture Radar Imagery V
Edmund G. Zelnio, Editor(s)

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