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Proceedings Paper

Evaluation of matrix factorization method for data reduction and the unsupervised clustering of hyperspectral data using second-order statistics
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Paper Abstract

We investigate a hyperspectral data reduction technique based on a matrix factorization method using the notion of linear independence instead of information measure, as an alternative to Principal Component Analysis (PCA) or the Karhunen-Loeve Transform. The technique is applied to a hyperspectral database whose spectral samples are known. We proceed to cluster such dimension-reduced databases with an unsupervised second order statistics clustering method and we compare those results to those produced by first order statistics. We illustrate the above methodology by applying it to several spectral databases. Since we know the class to which each sample belongs to in the database, we can effectively assess the algorithms' clustering/classification accuracy. In addition to using unsupervised clustering of data for purposes of image segmentation, we investigate this algorithm as a means for improving the integrity of spectral databases by removing spurious samples.

Paper Details

Date Published: 20 August 2001
PDF: 12 pages
Proc. SPIE 4381, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII, (20 August 2001); doi: 10.1117/12.437020
Show Author Affiliations
Edward Howard Bosch, U.S. Army Engineer Research and Development Ctr. (United States)
Robert S. Rand, U.S. Army Engineer Research and Development Ctr. (United States)


Published in SPIE Proceedings Vol. 4381:
Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII
Sylvia S. Shen; Michael R. Descour, Editor(s)

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