Hyperspectral analysis paper, 2004

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Title:

Statistical detection algorithms in fat-tailed hyperspectral background clutter

Abstract:

This paper explores three related themes:

  1. The statistical nature of hyperspectral background clutter;
  2. Why should it be like this, and
  3. How to exploit it in algorithms.

We begin by reviewing the evidence for the non-Gaussian and in particular fattailed 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.

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Authors:

M. Bernhardt, Waterfall Solutions, UK;
W. J. Oxford, Defence Science & Technology Laboratory, Farnborough, UK;
P. E. Clare, Defence Science & Technology Laboratory, Farnborough, UK;
V. A. Wilkinson, Defence Science & Technology Laboratory, Farnborough, UK;
D. G. Clarke, Defence Science & Technology Laboratory, Farnborough, UK;

Conferences:

Remote Sensing Europe 2004, 13 - 16 September, Maspalomas, Spain. Conference 5573: Image and Signal Processing for Remote Sensing IX.