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Proceedings Paper

Unsupervised hyperspectral image analysis using an advanced mixture model
Author(s): Bradley S. Denney; Katia Estabridis; Rui J. P. de Figueiredo
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Paper Abstract

Hyperspectral image analysis is an important component of advanced hyperspectral image understanding. We present a new approach that identifies unique materials and the abundance of these materials in a hyperspectral image. This approach uses physical constraints on material abundances and reflectances, and avoids the presence of a dark material class by parameterizing pixel illumination. The results are optimally generated in both supervised and unsupervised modes. Applications of the image analysis approach are also presented.

Paper Details

Date Published: 23 September 2003
PDF: 10 pages
Proc. SPIE 5093, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX, (23 September 2003); doi: 10.1117/12.487492
Show Author Affiliations
Bradley S. Denney, Neural Computing Systems (United States)
Katia Estabridis, Neural Computing Systems (United States)
Rui J. P. de Figueiredo, Neural Computing Systems (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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