Analytic performance model for grayscale quantization in the presence of additive noise

Adam R. Nolan; G. Steven Goley

doi:10.1117/12.884131

19 May 2011 Analytic performance model for grayscale quantization in the presence of additive noise

Adam R. Nolan, G. Steven Goley

Proceedings Volume 8049, Automatic Target Recognition XXI; 804910 (2011) https://doi.org/10.1117/12.884131
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

Synthetic aperture radar (SAR) exploitation algorithms typically rely on the use of derived features to represent the target. These features are chosen to discriminate between target classes while exhibiting robustness to noise and calibration artifacts. One of the challenges in working with such features, is understanding when this assumption of robustness is no longer valid. In this paper, we focus on characterizing the performance of the gray scale quantization feature in the presence of additive noise. We derive an approximation for the variance of the intraclass distance by treating the additive noise as an independently identically distributed (iid) process. The analytic model is contrasted with empirical results for a two class problem.

Citation Download Citation

Adam R. Nolan and G. Steven Goley "Analytic performance model for grayscale quantization in the presence of additive noise", Proc. SPIE 8049, Automatic Target Recognition XXI, 804910 (19 May 2011); https://doi.org/10.1117/12.884131

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