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Proceedings Paper

Validation of SAR ATR performance prediction using learned distortion models
Author(s): Michael Boshra; Bir Bhanu
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Paper Abstract

Performance prediction of SAR ATR has been a challenging problem. In our previous work, we developed a statistical framework for predicting bounds on fundamental performance of vote-based SAR ATR using scattering centers. This framework considered data distortion factors such as uncertainty, occlusion and clutter, in addition to model similarity. In this paper, we present an initial study on learning the statistical distributions of these factors. We focus on the development of a method for learning the distribution of a parameter that encodes the combined effect of the occlusion and similarity factors on performance. The impact of incorporating such a distribution on the accuracy of the predicted bounds is demonstrated by comparing bounds obtained using it with those obtained assuming simplified distributions. The data used in the experiments are obtained from the MSTAR public domain under different configurations and depression angles.

Paper Details

Date Published: 24 August 2000
PDF: 9 pages
Proc. SPIE 4053, Algorithms for Synthetic Aperture Radar Imagery VII, (24 August 2000); doi: 10.1117/12.396366
Show Author Affiliations
Michael Boshra, Univ. of California/Riverside (United States)
Bir Bhanu, Univ. of California/Riverside (United States)


Published in SPIE Proceedings Vol. 4053:
Algorithms for Synthetic Aperture Radar Imagery VII
Edmund G. Zelnio, Editor(s)

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