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Proceedings Paper

Automated intelligent distillation of hyperspectral imagery
Author(s): Robert L. Huguenin; Michael S. Bouchard; Mo-Hwa Wang; Mark A. Karaska
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Paper Abstract

Only a fraction of the bands in a hyperspectral image are typically needed to discriminate materials of interest for any given application. Many of the bands are in superfluous portions of the spectrum, where no substantive differences exist between the material spectra. Other bands are spread across the widths of the absorption/emission features, causing them to be highly correlated and of minimal added discrimination value. It would be desirable to be able to efficiently identify which bands are the most discriminating for a specific application, and to process only those image planes to detect the material of interest. The volume of data that needs to be processed would be reduced, discrimination sensitivity could be increased through elimination of extraneous bands, and multispectral exploitation algorithms could be used. Hyperspectral Distiller performs this function using only representative spectra of the materials to be discriminated as input. Distiller utilizes the BANDS algorithm to identify the wavelength positions, widths, and strengths of constituent absorption/emission features in the input spectra. Distiller then uses this information to characterize and rank each band according to its discrimination potential. The most discriminating bands (user defined number) are reported as output, and a subset image is created.

Paper Details

Date Published: 27 October 1999
PDF: 6 pages
Proc. SPIE 3753, Imaging Spectrometry V, (27 October 1999); doi: 10.1117/12.366269
Show Author Affiliations
Robert L. Huguenin, Applied Analysis Inc. (United States)
Michael S. Bouchard, Applied Analysis Inc. (United States)
Mo-Hwa Wang, Applied Analysis Inc. (United States)
Mark A. Karaska, Applied Analysis Inc. (United States)


Published in SPIE Proceedings Vol. 3753:
Imaging Spectrometry V
Michael R. Descour; Sylvia S. Shen, Editor(s)

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