1 June 2005 New models for hyperspectral anomaly detection and un-mixing
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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 new anomaly detection and un-mixing algorithms. The performance of these algorithms is compared with more traditional approaches.
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M. Bernhardt, J. P. Heather, M. I. Smith, "New models for hyperspectral anomaly detection and un-mixing", Proc. SPIE 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, (1 June 2005); doi: 10.1117/12.601738; https://doi.org/10.1117/12.601738
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