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

Edward Howard Bosch; Robert S. Rand

doi:10.1117/12.437020

20 August 2001 Evaluation of matrix factorization method for data reduction and the unsupervised clustering of hyperspectral data using second-order statistics

Edward Howard Bosch, Robert S. Rand

Author Affiliations +

Proceedings Volume 4381, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII; (2001) https://doi.org/10.1117/12.437020
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

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.

Citation Download Citation

Edward Howard Bosch and Robert S. Rand "Evaluation of matrix factorization method for data reduction and the unsupervised clustering of hyperspectral data using second-order statistics", Proc. SPIE 4381, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII, (20 August 2001); https://doi.org/10.1117/12.437020

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