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Proceedings Paper

Wavelet-based Markov models for clutter characterization in IR and SAR images
Author(s): Derek Stanford; James W. Pitton; Jill R. Goldschneider
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Paper Abstract

This paper presents wavelet-based methods for characterizing clutter in IR and SAR images. With our methods, the operating parameters of automatic target recognition (ATR) systems can automatically adapt to local clutter conditions. Structured clutter, which can confuse ATR systems, possesses correlation across scale in the wavelet domain. We model this correlation using wavelet-domain hidden Markov trees, for which efficient parameter estimation algorithms exist. Based on these models, we develop analytical methods for estimating the false alarm rates of mean-squared-error classifiers. These methods are equally useful for determining threshold levels for constant false alarm rate detectors.

Paper Details

Date Published: 5 April 2000
PDF: 15 pages
Proc. SPIE 4056, Wavelet Applications VII, (5 April 2000); doi: 10.1117/12.381701
Show Author Affiliations
Derek Stanford, MathSoft, Inc. (United States)
James W. Pitton, Univ. of Washington (United States)
Jill R. Goldschneider, MathSoft, Inc. (United States)


Published in SPIE Proceedings Vol. 4056:
Wavelet Applications VII
Harold H. Szu; Martin Vetterli; William J. Campbell; James R. Buss, Editor(s)

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