Support vector machines for SAR automatic target recognition
Zhao, Q.
Principe, J.C.
Comput. NeuroEng. Lab., Florida Univ., Gainesville, FL;
This paper appears in: Aerospace and Electronic Systems, IEEE Transactions on
Publication Date: Apr 2001
Volume: 37,
Issue: 2
On page(s): 643-654
ISSN: 0018-9251
References Cited: 41
CODEN: IEARAX
INSPEC Accession Number: 7001873
Digital Object Identifier: 10.1109/7.937475
Current Version Published: 2002-08-07
Abstract
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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