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.
Support vector machines for SAR automatic target recognition
IEEE Transactions on Aerospace and Electronic Systems ; 37 , 2 ; 643-654
2001-04-01
1558000 byte
Article (Journal)
Electronic Resource
English
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