Incorporating virtual negative examples to improve SAR ATR

Qun Zhao; Jose C. Principe

doi:10.1117/12.396347

24 August 2000 Incorporating virtual negative examples to improve SAR ATR

Qun Zhao, Jose C. Principe

Proceedings Volume 4053, Algorithms for Synthetic Aperture Radar Imagery VII; (2000) https://doi.org/10.1117/12.396347
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

One common problem in automatic target recognition (ATR) is the insufficient size of the training set. Methods have been proposed to counter act this shortcoming, such as the noisy interpolation theory, hints, new distance measure (tangent distance), virtual examples, etc. This paper presents the idea of creating virtual negative examples as severe distortions of the known class patterns. Two classifiers are studied, a perceptron and a Support Vector Machine (SVM) trained to recognize objects in synthetic aperture radar (SAR) images. They utilize the training set (positive examples) to create the discriminant function of each class in the conventional way. On the other hand, the virtual negative examples will help determine the regions where the discriminant function should yield a low value. The experimental results show that incorporating the negative examples improves greatly (nearly 50 percents improvement) the confuser rejection rates.

Citation Download Citation

Qun Zhao and Jose C. Principe "Incorporating virtual negative examples to improve SAR ATR", Proc. SPIE 4053, Algorithms for Synthetic Aperture Radar Imagery VII, (24 August 2000); https://doi.org/10.1117/12.396347

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