Robust automatic clustering of hyperspectral imagery using non-Gaussian mixtures

Michael D. Farrell Jr.; Russell M. Mersereau

doi:10.1117/12.565567

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

Michael D. Farrell Jr., Russell M. Mersereau

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.

Citation Download Citation

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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