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Conference Paper  May 1, 2016

Analyzing hyperspectral images into multiple subspaces using Gaussian mixture models

SRI Authors Clay Spence

Citation

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Spence, Clay D. “Analyzing hyperspectral images into multiple subspaces using Gaussian mixture models.” SPIE Defense+ Security. International Society for Optics and Photonics, 2016.

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.

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