2 September 2004 Radar clutter modeling for change detection
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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.
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Erik P. Blasch, Erik P. Blasch, Mike Hensel, Mike Hensel, James L. Jackson, James L. Jackson, } "Radar clutter modeling for change detection", Proc. SPIE 5427, Algorithms for Synthetic Aperture Radar Imagery XI, (2 September 2004); doi: 10.1117/12.542857; https://doi.org/10.1117/12.542857

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