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Optical Engineering

Increasing the discrimination of synthetic aperture radar recognition models
Author(s): Bir Bhanu; Grinnell Jones
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Paper Abstract

The focus of this work is optimizing recognition models for synthetic aperture radar (SAR) signatures of vehicles to improve the performance of a recognition algorithm under the extended operating conditions of target articulation, occlusion, and configuration variants. The recognition models are based on quasi-invariant local features, scattering center locations, and magnitudes. The approach determines the similarities and differences among the various vehicle models. Methods to penalize similar features or reward dissimilar features are used to increase the distinguishability of the recognition model instances. Extensive experimental recognition results are presented in terms of confusion matrices and receiver operating characteristic (ROC) curves to show the improvements in recognition performance for real SAR signatures of vehicle targets with articulation, configuration variants, and occlusion.

Paper Details

Date Published: 1 December 2002
PDF: 9 pages
Opt. Eng. 41(12) doi: 10.1117/1.1517286
Published in: Optical Engineering Volume 41, Issue 12
Show Author Affiliations
Bir Bhanu, Univ. of California/Riverside (United States)
Grinnell Jones, Univ. of California/Riverside (United States)


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