Detection and classification of MSTAR objects via morphological shared-weight neural networks

Nipon Theera-Umpon; Mohamed A. Khabou; Paul D. Gader; James M. Keller; Hongchi Shi; Hongzheng Li

doi:10.1117/12.321856

15 September 1998 Detection and classification of MSTAR objects via morphological shared-weight neural networks

Nipon Theera-Umpon, Mohamed A. Khabou, Paul D. Gader, James M. Keller, Hongchi Shi, Hongzheng Li

Proceedings Volume 3370, Algorithms for Synthetic Aperture Radar Imagery V; (1998) https://doi.org/10.1117/12.321856
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

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.

Citation Download Citation

Nipon Theera-Umpon, Mohamed A. Khabou, Paul D. Gader, James M. Keller, Hongchi Shi, and Hongzheng Li "Detection and classification of MSTAR objects via morphological shared-weight neural networks", Proc. SPIE 3370, Algorithms for Synthetic Aperture Radar Imagery V, (15 September 1998); https://doi.org/10.1117/12.321856

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