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Proceedings Paper

Incorporating virtual negative examples to improve SAR ATR
Author(s): Qun Zhao; Jose C. Principe
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Paper Abstract

One common problem in automatic target recognition (ATR) is the insufficient size of the training set. Methods have been proposed to counter act this shortcoming, such as the noisy interpolation theory, hints, new distance measure (tangent distance), virtual examples, etc. This paper presents the idea of creating virtual negative examples as severe distortions of the known class patterns. Two classifiers are studied, a perceptron and a Support Vector Machine (SVM) trained to recognize objects in synthetic aperture radar (SAR) images. They utilize the training set (positive examples) to create the discriminant function of each class in the conventional way. On the other hand, the virtual negative examples will help determine the regions where the discriminant function should yield a low value. The experimental results show that incorporating the negative examples improves greatly (nearly 50 percents improvement) the confuser rejection rates.

Paper Details

Date Published: 24 August 2000
PDF: 7 pages
Proc. SPIE 4053, Algorithms for Synthetic Aperture Radar Imagery VII, (24 August 2000); doi: 10.1117/12.396347
Show Author Affiliations
Qun Zhao, Univ. of Florida (United States)
Jose C. Principe, Univ. of Florida (United States)


Published in SPIE Proceedings Vol. 4053:
Algorithms for Synthetic Aperture Radar Imagery VII
Edmund G. Zelnio, Editor(s)

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