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Proceedings Paper

An empirical estimate of the multivariate normality of hyperspectral image data
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Paper Abstract

Historically, much of spectral image analysis revolves around assumptions of multivariate normality. If the background spectral distribution can be assumed to be multivariate normal, then algorithms for anomaly detection, target detection, and classification can be developed around that assumption. However, as the current generation sensors typically have higher spatial and/or spectral resolution, the spectral distribution complexity of the data collected is increasing and these assumptions are no longer adequate, particularly image-wide. However, large portions of the imagery may be accurately described by a multivariate normal distribution. A new empirical method for assessing the multivariate normality of a hyperspectral distribution is presented here. This method assesses the multivariate normality of individual spectral image tiles and is applied to the large area search problem. Additionally, the methodology is applied to a selection of full hyperspectral data sets for general content evaluation. This information can be used to indicate the degree of multivariate normality (or complexity) of the data or data regions and to determine the appropriate algorithm to use globally or locally for spatially adaptive processing.

Paper Details

Date Published: 20 May 2011
PDF: 11 pages
Proc. SPIE 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 80481J (20 May 2011); doi: 10.1117/12.881642
Show Author Affiliations
Ariel Schlamm, Rochester Institute of Technology (United States)
David Messinger, Rochester 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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