24 August 1999 From hyperplanes to large-margin classifiers: applications of SAR ATR
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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.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Qun Zhao, Qun Zhao, Jose C. Principe, Jose C. Principe, Dongxin Xu, Dongxin Xu, } "From hyperplanes to large-margin classifiers: applications of SAR ATR", Proc. SPIE 3718, Automatic Target Recognition IX, (24 August 1999); doi: 10.1117/12.359940; https://doi.org/10.1117/12.359940

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