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Proceedings Paper

A comparison study of dimension estimation algorithms
Author(s): Ariel Schlamm; Ronald G. Resmini; David Messinger; William Basener
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Paper Abstract

The inherent dimension of hyperspectral data is commonly estimated for the purpose of dimension reduction. However, the dimension estimate itself may be a useful measure for extracting information about hyperspectral data, including scene content, complexity, and clutter. There are many ways to estimate the inherent dimension of data, each measuring the data in a different way. This paper compares a group of dimension estimation metrics on a variety of data, both full scene and individual material regions, to determine the relationship between the different estimates and what features each method is measuring when applied to complex data.

Paper Details

Date Published: 12 May 2010
PDF: 8 pages
Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 76952D (12 May 2010); doi: 10.1117/12.849125
Show Author Affiliations
Ariel Schlamm, Rochester Institute of Technology (United States)
Ronald G. Resmini, George Mason Univ. (United States)
David Messinger, Rochester Institute of Technology (United States)
William Basener, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 7695:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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