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Proceedings Paper

Analyzing hyperspectral images into multiple subspaces using Gaussian mixture models
Author(s): Clay D. Spence
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Paper Abstract

I argue that the spectra in a hyperspectral datacube will usually lie in several low-dimensional subspaces, and that these subspaces are more easily estimated from the data than the endmembers. I present an algorithm for finding the subspaces. The algorithm fits the data with a Gaussian mixture model, in which the means and covariance matrices are parameterized in terms of the subspaces. The locations of materials can be inferred from the fit of library spectra to the subspaces. The algorithm can be modified to perform material detection. This has better performance than standard algorithms such as ACE, and runs in real time.

Paper Details

Date Published: 17 May 2016
PDF: 14 pages
Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 98401A (17 May 2016); doi: 10.1117/12.2224234
Show Author Affiliations
Clay D. Spence, SRI International Sarnoff (United States)


Published in SPIE Proceedings Vol. 9840:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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