Waterfall Solutions : Publication Abstract

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Title

New models for hyperspectral un-mixing

Abstract:

It is now established that hyperspectral images of many natural backgrounds have statistics with fat-tails. In spite of this many of the algorithms that are used to process them appeal to the multivariate Gaussian model.

In this paper we consider biologically motivated generative models that might explain observed mixtures of vegetation in natural backgrounds. The degree to which these models match the observed fat-tailed distributions is investigated.

Having shown how fat-tailed statistics arise naturally from the generative process the models are put to work in a new un-mixing algorithm.

The performance of this algorithm is compared with more traditional approaches to hyperspectral un-mixing.

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Authors:

M. Bernhardt, Waterfall Solutions, UK;
J. P. Heather, Waterfall Solutions, UK;

Conferences:

Defense and Security Symposium 2005, Conference 5806: Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI : Spectral Unmixing