SVM classifier applied to the MSTAR public data set

Michael Lee Bryant; Frederick D. Garber

doi:10.1117/12.357652

13 August 1999 SVM classifier applied to the MSTAR public data set

Michael Lee Bryant, Frederick D. Garber

Proceedings Volume 3721, Algorithms for Synthetic Aperture Radar Imagery VI; (1999) https://doi.org/10.1117/12.357652
Event: AeroSense '99, 1999, Orlando, FL, United States

Abstract

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.

Citation Download Citation

Michael Lee Bryant and Frederick D. Garber "SVM classifier applied to the MSTAR public data set", Proc. SPIE 3721, Algorithms for Synthetic Aperture Radar Imagery VI, (13 August 1999); https://doi.org/10.1117/12.357652

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available