Robust automatic clustering of hyperspectral imagery using non-Gaussian mixtures

Michael D. Farrell Jr.; Russell M. Mersereau

doi:10.1117/12.565567

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10 November 2004 Robust automatic clustering of hyperspectral imagery using non-Gaussian mixtures

Michael D. Farrell Jr., Russell M. Mersereau

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Proceedings Volume 5573, Image and Signal Processing for Remote Sensing X; (2004) https://doi.org/10.1117/12.565567
Event: Remote Sensing, 2004, Maspalomas, Canary Islands, Spain

Abstract

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); https://doi.org/10.1117/12.565567

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