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Journal of the Optical Society of America A

Journal of the Optical Society of America A


  • Vol. 25, Iss. 12 — Dec. 1, 2008
  • pp: 3001–3012

Tensor methods for hyperspectral data analysis: a space object material identification study

Qiang Zhang, Han Wang, Robert J. Plemmons, and V. Pau'l Pauca  »View Author Affiliations

JOSA A, Vol. 25, Issue 12, pp. 3001-3012 (2008)

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An important and well-studied problem in hyperspectral image data applications is to identify materials present in the object or scene being imaged and to quantify their abundance in the mixture. Due to the increasing quantity of data usually encountered in hyperspectral datasets, effective data compression is also an important consideration. In this paper, we develop novel methods based on tensor analysis that focus on all three of these goals: material identification, material abundance estimation, and data compression. Test results are reported in all three perspectives.

© 2008 Optical Society of America

OCIS Codes
(000.3860) General : Mathematical methods in physics
(100.6890) Image processing : Three-dimensional image processing
(100.3008) Image processing : Image recognition, algorithms and filters
(110.4234) Imaging systems : Multispectral and hyperspectral imaging

ToC Category:
Image Processing

Original Manuscript: May 30, 2008
Revised Manuscript: August 15, 2008
Manuscript Accepted: September 26, 2008
Published: November 18, 2008

Qiang Zhang, Han Wang, Robert J. Plemmons, and V. Pau'l Pauca, "Tensor methods for hyperspectral data analysis: a space object material identification study," J. Opt. Soc. Am. A 25, 3001-3012 (2008)

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