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

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

http://dx.doi.org/10.1364/JOSAA.25.003001

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### Abstract

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

**History**

Original Manuscript: May 30, 2008

Revised Manuscript: August 15, 2008

Manuscript Accepted: September 26, 2008

Published: November 18, 2008

**Citation**

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)

http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-25-12-3001

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