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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