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Proceedings Paper

Material identification and classification in hyperspectral imagery using the normal compositional model
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Paper Abstract

The normal compositional model (NCM) simultaneously models subpixel mixing and intra-class variation in multidimensional imagery. It may be used as the foundation for the derivation of supervised and unsupervised classification and detection algorithms. Results from applying the algorithm to AVIRIS SWIR data collected over Cuprite, Nevada are described. The NCM class means are compared with library spectra using the Tetracorder algorithm. Of the eighteen classes used to model the data, eleven are associated with minerals that are known to be in the scene and are distinguishable in the SWIR, five are identified with Fe-bearing minerals that are not further classifiable using SWIR data, and the remaining two are spatially diffuse mixtures. The NCM classes distinguish (1) high and moderate temperature alunites, (2) dickite and kaolinite, and (3) high and moderate aluminum concentration muscovite. Estimated abundance maps locate many of the known mineral features. Furthermore, the NCM class means are compared with corresponding endmembers estimated using a linear mixture model (LMM). Of the eleven identifiable (NCM class mean, LMM endmember) pairs, ten are consistently identified, while the NCM estimation procedure reveals a diagnostic feature of the eleventh that is more obscure in the corresponding endmember and results in conflicting identifications.

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: 10.1117/12.488331
Show Author Affiliations
David Walter Jacques Stein, MIT Lincoln Lab. (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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