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Journal of the Optical Society of America A

Journal of the Optical Society of America A


  • Vol. 21, Iss. 6 — Jun. 1, 2004
  • pp: 1026–1034

Using independent component analysis for material estimation in hyperspectral images

Chia-Yun Kuan and Glenn Healey  »View Author Affiliations

JOSA A, Vol. 21, Issue 6, pp. 1026-1034 (2004)

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We develop a method for automated material estimation in hyperspectral images. The method models a hyperspectral pixel as a linear mixture of unknown materials. The method is particularly useful for applications in which material regions in a scene are smaller than one pixel. In contrast to many material estimation methods, the new method uses the statistics of large numbers of pixels rather than attempting to identify a small number of the purest pixels. The method is based on maximizing the independence of material abundances at each pixel. We show how independent component analysis algorithms can be adapted for use with this problem. We demonstrate properties of the method by application to airborne hyperspectral data.

© 2004 Optical Society of America

OCIS Codes
(100.0100) Image processing : Image processing
(280.0280) Remote sensing and sensors : Remote sensing and sensors

Original Manuscript: November 12, 2003
Revised Manuscript: January 6, 2004
Manuscript Accepted: January 6, 2004
Published: June 1, 2004

Chia-Yun Kuan and Glenn Healey, "Using independent component analysis for material estimation in hyperspectral images," J. Opt. Soc. Am. A 21, 1026-1034 (2004)

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