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10 November 2004 Robust automatic clustering of hyperspectral imagery using non-Gaussian mixtures
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This paper addresses the utility of robust automatic clustering of hyperspectral image data. Such clustering is possible only when the background in a scene is accurately modeled. Mixtures of non-Gaussian densities have been discussed recently, and here we move further down this path. We derive a t mixture model for the background in hyperspectral images, using two techniques for estimating parameters based on the Expectation-Maximization algorithm. Visual and statistical evaluation of these techniques are made with AVIRIS data. Dealing with the data's inhomogeneity by developing proper models of the background (i.e. clutter) in a hyperspectral image is important in target detection applications, especially for accurate performance prediction and detector analysis.
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Michael D. Farrell Jr. and Russell M. Mersereau "Robust automatic clustering of hyperspectral imagery using non-Gaussian mixtures", Proc. SPIE 5573, Image and Signal Processing for Remote Sensing X, (10 November 2004);

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