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Proceedings Paper

Selecting training and test images for optimized anomaly detection algorithms in hyperspectral imagery through robust parameter design
Author(s): Frank M. Mindrup; Mark A. Friend; Kenneth W. Bauer
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Paper Abstract

There are numerous anomaly detection algorithms proposed for hyperspectral imagery. Robust parameter design (RPD) techniques have been applied to some of these algorithms in an attempt to choose robust settings capable of operating consistently across a large variety of image scenes. Typically, training and test sets of hyperspectral images are chosen randomly. Previous research developed a frameworkfor optimizing anomaly detection in HSI by considering specific image characteristics as noise variables within the context of RPD; these characteristics include the Fisher's score, ratio of target pixels and number of clusters. This paper describes a method for selecting hyperspectral image training and test subsets yielding consistent RPD results based on these noise features. These subsets are not necessarily orthogonal, but still provide improvements over random training and test subset assignments by maximizing the volume and average distance between image noise characteristics. Several different mathematical models representing the value of a training and test set based on such measures as the D-optimal score and various distance norms are tested in a simulation experiment.

Paper Details

Date Published: 20 May 2011
PDF: 15 pages
Proc. SPIE 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 80480C (20 May 2011); doi: 10.1117/12.884120
Show Author Affiliations
Frank M. Mindrup, Air Force Institute of Technology (United States)
Mark A. Friend, Air Force Institute of Technology (United States)
Kenneth W. Bauer, Air Force Institute of Technology (United States)

Published in SPIE Proceedings Vol. 8048:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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