From hyperplanes to large-margin classifiers: applications of SAR ATR

Qun Zhao; Jose C. Principe; Dongxin Xu

doi:10.1117/12.359940

24 August 1999 From hyperplanes to large-margin classifiers: applications of SAR ATR

Qun Zhao, Jose C. Principe, Dongxin Xu

Proceedings Volume 3718, Automatic Target Recognition IX; (1999) https://doi.org/10.1117/12.359940
Event: AeroSense '99, 1999, Orlando, FL, United States

Abstract

In this paper, the structural risk minimization (SRM) criterion is employed to train a large margin classifier, the support vector machine (SVM). Its relative performance is compared with traditional classifiers employing hyperplanes against a realistic difficult problem, the synthetic aperture radar (SAR) automatic target recognition (ATR). In most pattern recognition applications, the task is to perform classification into a fixed number of classes. However, in some practical cases, such as ATR, one also needs to carry out a reliable pattern rejection. Experimental results showed that the SVM with the Gaussian kernels performs well in target recognition. Moreover, the SVM is able to form a local or 'bounded' decision region that presents better rejection to confusers.

Citation Download Citation

Qun Zhao, Jose C. Principe, and Dongxin Xu "From hyperplanes to large-margin classifiers: applications of SAR ATR", Proc. SPIE 3718, Automatic Target Recognition IX, (24 August 1999); https://doi.org/10.1117/12.359940

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