Statistical detection algorithms in fat-tailed hyperspectral background clutter

Mark Bernhardt; William J. Oxford; Philip E. Clare; Vicky A. Wilkinson; Damien G. Clarke

doi:10.1117/12.565537

10 November 2004 Statistical detection algorithms in fat-tailed hyperspectral background clutter

Mark Bernhardt, William J. Oxford, Philip E. Clare, Vicky A. Wilkinson, Damien G. Clarke

Proceedings Volume 5573, Image and Signal Processing for Remote Sensing X; (2004) https://doi.org/10.1117/12.565537
Event: Remote Sensing, 2004, Maspalomas, Canary Islands, Spain

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.

Citation Download Citation

Mark Bernhardt, William J. Oxford, Philip E. Clare, Vicky A. Wilkinson, and Damien G. Clarke "Statistical detection algorithms in fat-tailed hyperspectral background clutter", Proc. SPIE 5573, Image and Signal Processing for Remote Sensing X, (10 November 2004); https://doi.org/10.1117/12.565537

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