Composite class models for SAR recognition

Bir Bhanu; Grinnell Jones III; Rong Wang

doi:10.1117/12.487532

12 September 2003 Composite class models for SAR recognition

Bir Bhanu, Grinnell Jones III, Rong Wang

Proceedings Volume 5095, Algorithms for Synthetic Aperture Radar Imagery X; (2003) https://doi.org/10.1117/12.487532
Event: AeroSense 2003, 2003, Orlando, Florida, United States

Abstract

This paper focuses on a genetic algorithm based method that automates the construction of local feature based composite class models to capture the salient characteristics of configuration variants of vehicle targets in SAR imagery and increase the performance of SAR recognition systems. The recognition models are based on quasi-invariant local features: SAR scattering center locations and magnitudes. The approach uses an efficient SAR recognition system as an evaluation function to determine the fitness class models. Experimental results are given on the fitness of the composite models and the similarity of both the original training model configurations and the synthesized composite models to the test configurations. In addition, results are presented to show the SAR recognition variants of MSTAR vehicle targets.

Citation Download Citation

Bir Bhanu, Grinnell Jones III, and Rong Wang "Composite class models for SAR recognition", Proc. SPIE 5095, Algorithms for Synthetic Aperture Radar Imagery X, (12 September 2003); https://doi.org/10.1117/12.487532

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