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Proceedings Paper

Analyzing hyperspectral data with independent component analysis
Author(s): Jessica D. Bayliss; J. Anthony Gualtieri; Robert F. Cromp
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Paper Abstract

Hyperspectral image sensors provide images with a large number of contiguous spectral channels per pixel and enable information about different materials within a pixel to be obtained. The problem of spectrally unmixing materials may be viewed as a specific case of the blind source separation problem where data consists of mixed signals and the goal is to determine the contribution of each mineral to the mix without prior knowledge of the minerals in the mix. The technique of independent component analysis (ICA) assumes that the spectral components are close to statistically independent and provides an unsupervised method for blind source separation. We introduce contextual ICA in the context of hyperspectral data analysis and apply the method to mineral data from synthetically mixed minerals and real image signatures.

Paper Details

Date Published: 1 March 1998
PDF: 11 pages
Proc. SPIE 3240, 26th AIPR Workshop: Exploiting New Image Sources and Sensors, (1 March 1998); doi: 10.1117/12.300050
Show Author Affiliations
Jessica D. Bayliss, Univ. of Rochester (United States)
J. Anthony Gualtieri, NASA Goddard Space Flight Ctr. (United States)
Robert F. Cromp, NASA Goddard Space Flight Ctr. (United States)


Published in SPIE Proceedings Vol. 3240:
26th AIPR Workshop: Exploiting New Image Sources and Sensors
J. Michael Selander, Editor(s)

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