Support vector machines for SAR automatic target recognition

Author

Zhao, Qun ; Principe, Jose C.

Author_Institution

Comput. NeuroEng. Lab., Florida Univ., Gainesville, FL, USA

Volume

37

Issue

2

fYear

2001

fDate

4/1/2001 12:00:00 AM

Firstpage

643

Lastpage

654

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

Keywords

image classification; learning automata; radar computing; radar imaging; synthetic aperture radar; Gaussian kernels; SAR automatic target recognition; classifiers; support vector machines; synthetic aperture radar; target classification; Kernel; Machine learning; Pattern recognition; Risk management; Support vector machine classification; Support vector machines; Synthetic aperture radar; Target recognition; Testing; Virtual colonoscopy;

fLanguage

English

Journal_Title

Aerospace and Electronic Systems, IEEE Transactions on

Publisher

ieee

ISSN

0018-9251

Type

jour

DOI

10.1109/7.937475

Filename

937475