Modeling performance and image collection utility for multiple look ATR

William C. Snyder; Gil J. Ettinger; S. Laprise

doi:10.1117/12.555517

2 September 2004 Modeling performance and image collection utility for multiple look ATR

William C. Snyder, Gil J. Ettinger, S. Laprise

Author Affiliations +

Proceedings Volume 5427, Algorithms for Synthetic Aperture Radar Imagery XI; (2004) https://doi.org/10.1117/12.555517
Event: Defense and Security, 2004, Orlando, Florida, United States

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.

Citation Download Citation

William C. Snyder, Gil J. Ettinger, and S. Laprise "Modeling performance and image collection utility for multiple look ATR", Proc. SPIE 5427, Algorithms for Synthetic Aperture Radar Imagery XI, (2 September 2004); https://doi.org/10.1117/12.555517

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