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Proceedings Paper

Robust automatic clustering of hyperspectral imagery using non-Gaussian mixtures
Author(s): Michael D. Farrell; Russell M. Mersereau
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Paper 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.

Paper Details

Date Published: 10 November 2004
PDF: 12 pages
Proc. SPIE 5573, Image and Signal Processing for Remote Sensing X, (10 November 2004); doi: 10.1117/12.565567
Show Author Affiliations
Michael D. Farrell, Georgia Institute of Technology (United States)
Russell M. Mersereau, Georgia Institute of Technology (United States)


Published in SPIE Proceedings Vol. 5573:
Image and Signal Processing for Remote Sensing X
Lorenzo Bruzzone, Editor(s)

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