12 May 2010 A comparison study of dimension estimation algorithms
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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.
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Ariel Schlamm, Ronald G. Resmini, David Messinger, William Basener, "A comparison study of dimension estimation algorithms", Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 76952D (12 May 2010); doi: 10.1117/12.849125; https://doi.org/10.1117/12.849125
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