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Proceedings Paper

Radar clutter modeling for change detection
Author(s): Erik P. Blasch; Mike Hensel; James L. Jackson
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Paper Abstract

To recognize an object in an image, an algorithm must identify not only the object pixels, but also non-object clutter pixels. Non-object pixels can be assessed with a priori clutter models that account for the varying terrain and cultural objects. Radar clutter models have been well developed; however, these models typically incorporate a single distribution to capture background effects. In this paper, we propose to use a fusion of distributions through mixture modeling to characterize various background clutter information so as to more accurately develop a clutter model useful for object recognition. In a radar example, we show a fused-distribution using a Rayleigh and Pareto model describing the average and heavy tail clutter characteristics.

Paper Details

Date Published: 2 September 2004
PDF: 10 pages
Proc. SPIE 5427, Algorithms for Synthetic Aperture Radar Imagery XI, (2 September 2004); doi: 10.1117/12.542857
Show Author Affiliations
Erik P. Blasch, Air Force Research Lab. (United States)
Mike Hensel, Jacobs Sverdrup (United States)
James L. Jackson, Jacobs Sverdrup (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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