Support vector machines for SAR automatic target recognition
Zhao, Q.; Principe, J.C.
Aerospace and Electronic Systems, IEEE Transactions on
Volume 37, Issue 2, Apr 2001 Page(s):643 - 654
Digital Object Identifier   10.1109/7.937475
Summary:Algorithms that produce classifiers with large margins, such as support vector machines (SVMs), AdaBoost, etc, are receiving more and more attention in the literature. A real application of SVMs for synthetic aperture radar automatic target recognition (SAR/ATR) is presented and the result is compared with conventional classifiers. The SVMs are tested for classification both in closed and open sets (recognition). Experimental results showed that SVMs outperform conventional classifiers in target classification. Moreover, SVMs with the Gaussian kernels are able to form a local “bounded” decision region around each class that presents better rejection to confusers

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