12 May 2010 A comparison study of dimension estimation algorithms
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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, Ariel Schlamm, Ronald G. Resmini, Ronald G. Resmini, David Messinger, David Messinger, William Basener, 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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