Automated intelligent distillation of hyperspectral imagery

Robert L. Huguenin; Michael S. Bouchard; Mo-Hwa Wang; Mark A. Karaska

doi:10.1117/12.366269

27 October 1999 Automated intelligent distillation of hyperspectral imagery

Robert L. Huguenin, Michael S. Bouchard, Mo-Hwa Wang, Mark A. Karaska

Proceedings Volume 3753, Imaging Spectrometry V; (1999) https://doi.org/10.1117/12.366269
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1999, Denver, CO, United States

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.

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Robert L. Huguenin, Michael S. Bouchard, Mo-Hwa Wang, and Mark A. Karaska "Automated intelligent distillation of hyperspectral imagery", Proc. SPIE 3753, Imaging Spectrometry V, (27 October 1999); https://doi.org/10.1117/12.366269

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KEYWORDS

Absorption

Image processing

Sensors

Hyperspectral imaging

Earth observing sensors

Landsat

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