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Proceedings Paper

Statistical detection algorithms in fat-tailed hyperspectral background clutter
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Paper Abstract

This paper explores three related themes: the statistical nature of hyperspectral background clutter; why should it be like this; and how to exploit it in algorithms. We begin by reviewing the evidence for the non-Gaussian and in particular fat-tailed nature of hyperspectral background distributions. Following this we develop a simple statistical model that gives some insight into why the observed fat tails occur. We demonstrate that this model fits the background data for some hyperspectral data sets. Finally we make use of the model to develop hyperspectral detection algorithms and compare them to traditional algorithms on some real world data sets.

Paper Details

Date Published: 10 November 2004
PDF: 11 pages
Proc. SPIE 5573, Image and Signal Processing for Remote Sensing X, (10 November 2004); doi: 10.1117/12.565537
Show Author Affiliations
Mark Bernhardt, Waterfall Solutions Ltd. (United Kingdom)
William J. Oxford, Defence Science and Technology Lab. (United Kingdom)
Philip E. Clare, Defence Science and Technology Lab. (United Kingdom)
Vicky A. Wilkinson, Defence Science and Technology Lab. (United Kingdom)
Damien G. Clarke, Defence Science and Technology Lab. (United Kingdom)


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

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