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Proceedings Paper

Modeling performance and image collection utility for multiple look ATR
Author(s): William C. Snyder; Gil J. Ettinger; S. Laprise
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Paper Abstract

We present a performance model for estimating the likelihood function and posterior probability of classes in a multiple-look SAR ATR classifier. We extend performance estimation to performance prediction in order to assess the effects of additional looks at different targets in a scene. This likelihood improvement model depends on a variety of factors including the resulting look angle diversity and the resolution of the sensor. The performance model parameters are estimated from classification scores and multi-look performance with real data, but could also be developed from simulations in cases where no data exist. Finally, we propose a transformation from the predicted performance to a value for each look that is used to optimize asset tasking. The value transformation is based on the target importance and absolute posterior probability.

Paper Details

Date Published: 2 September 2004
PDF: 12 pages
Proc. SPIE 5427, Algorithms for Synthetic Aperture Radar Imagery XI, (2 September 2004); doi: 10.1117/12.555517
Show Author Affiliations
William C. Snyder, ALPHATECH, Inc. (United States)
Gil J. Ettinger, ALPHATECH, Inc. (United States)
S. Laprise, ALPHATECH, Inc. (United States)


Published in SPIE Proceedings Vol. 5427:
Algorithms for Synthetic Aperture Radar Imagery XI
Edmund G. Zelnio; Frederick D. Garber, Editor(s)

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