SVM classifier applied to the MSTAR public data set

被引：40

作者：

Bryant, M ^{[1
]}

Garber, F ^{[1
]}

机构：

[1] USAF, AFRL, SNAT, Washington, DC 20330 USA

来源：

ALGORITHMS FOR SYNTHETIC APERTURE RADAR IMAGERY VI | 1999年 / 3721卷

关键词：

support vector machines; SVM; structural risk minimization; SRM; MSTAR; ATR;

D O I：

10.1117/12.357652

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

Support vector machines (SVM) are one of the most recent tools to be developed from research in statistical learning theory. The foundations of SVM were developed by Vapnik, and are gaining popularity within the learning theory community due to many attractive features and excellent demonstrated performance. However, SVM have not yet gained popularity within the synthetic aperture radar (SAR) automatic target recognition (ATR) community. The purpose of this paper is to introduce the concepts of SVM and to benchmark its performance on the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set.

引用

页码：355 / 360

页数：6