SAR automatic target recognition using maximum likelihood template-based classifiers

John A. Saghri

doi:10.1117/12.799417

15 September 2008 SAR automatic target recognition using maximum likelihood template-based classifiers

John A. Saghri

Proceedings Volume 7073, Applications of Digital Image Processing XXXI; 70731I (2008) https://doi.org/10.1117/12.799417
Event: Optical Engineering + Applications, 2008, San Diego, California, United States

Abstract

A review of several recently-developed maximum likelihood template-based automatic target recognition (ATR) algorithms for extended targets in synthetic aperture radar (SAR) imagery data is presented. The algorithms are based on 'gradient' peaks, 'ceiling' peaks, edges, corners, shadows, and rectangular-fits. A weight-based Bayesian maximum likelihood scheme to combine multiple template-based classifiers is presented. The feature weights are derived from prior recognition accuracies, i.e., confidence levels, achieved by individual template-based classifiers. Application of feature-based weights instead of target specific feature-based weights reduces the resulting ATR accuracy by only a small amount. Preliminary results indicate that (1) the ceiling peaks provide the most target-discriminating power, (2) inclusion of more target-discriminating features leads to higher classification accuracy. Dempster-Shaffer rule of combination is suggested as a potential alternative to the implemented Bayesian decision theory approach to resolve conflicting reports from multiple template-based classifiers.

Citation Download Citation

John A. Saghri "SAR automatic target recognition using maximum likelihood template-based classifiers", Proc. SPIE 7073, Applications of Digital Image Processing XXXI, 70731I (15 September 2008); https://doi.org/10.1117/12.799417

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