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Proceedings Paper

Optimum classification of correlated SAR textures
Author(s): Christopher John Oliver
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Paper Abstract

Previous studies on discriminating between SAR clutter textures have been largely based on optimizing the performance for the single-point statistics of texture alone. This paper makes a study of the effects of introducing spatial correlations on classification into a set of predefined textures. Maximum likelihood (ML) estimation is adopted throughout. Classification based on fitting the autocorrelation function is compared with that based on the amplitude spectrum of the data. In each case, care is taken to derive a good approximation to the observed data distributions so that the ML treatment is well founded.

Paper Details

Date Published: 17 December 1996
PDF: 10 pages
Proc. SPIE 2958, Microwave Sensing and Synthetic Aperture Radar, (17 December 1996); doi: 10.1117/12.262721
Show Author Affiliations
Christopher John Oliver, Defence Research Agency Malvern (United Kingdom)

Published in SPIE Proceedings Vol. 2958:
Microwave Sensing and Synthetic Aperture Radar
Giorgio Franceschetti; Christopher John Oliver; Franco S. Rubertone; Shahram Tajbakhsh, Editor(s)

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