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Proceedings Paper

Composite class models for SAR recognition
Author(s): Bir Bhanu; Grinnell Jones; Rong Wang
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Paper 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.

Paper Details

Date Published: 12 September 2003
PDF: 8 pages
Proc. SPIE 5095, Algorithms for Synthetic Aperture Radar Imagery X, (12 September 2003); doi: 10.1117/12.487532
Show Author Affiliations
Bir Bhanu, Univ. of California, Riverside (United States)
Grinnell Jones, Univ. of California, Riverside (United States)
Rong Wang, Univ. of California, Riverside (United States)


Published in SPIE Proceedings Vol. 5095:
Algorithms for Synthetic Aperture Radar Imagery X
Edmund G. Zelnio; Frederick D. Garber, Editor(s)

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