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Proceedings Paper

A physically constrained localized linear mixing model for TERCAT applications
Author(s): Robert S. Rand
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Paper Abstract

A physically-constrained localized linear mixing model suitable to process multi/hyperspectral imagery for Terrain Categorization (TERCAT) applications is investigated. Unlike the basic spectral linear mixing model that typically includes all potential endmembers in a set, simultaneously, in the model for each site in an image, the proposed approach restricts the local model at each site to a subset of endmembers, using localized spectral/spatial constraints to narrow the selection process. This approach is used to reduce the observed instability of conventional linear mixture analysis in addressing TERCAT problems for scenes with a large number of endmembers. Experiments are conducted on an 18 channel GERIS scene, airborne-collected over Northern Virginia, that contains a diverse range of terrain features, showing the benefit of this method as compared to the basic linear mixture analysis approach for TERCAT applications.

Paper Details

Date Published: 23 September 2003
PDF: 12 pages
Proc. SPIE 5093, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX, (23 September 2003); doi: 10.1117/12.485935
Show Author Affiliations
Robert S. Rand, U.S. Army Engineer Research and Development Ctr. (United States)


Published in SPIE Proceedings Vol. 5093:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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