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Proceedings Paper

Spectrally and spatially adaptive hyperspectral data compression
Author(s): Bernard V. Brower; David H. Hadcock; Joseph P. Reitz; John R. Schott
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Paper Abstract

A hyperspectral data compression algorithm is presented that utilizes a modular approach of an adaptive spectral transform to decorrelate the spectral bands, which are then adaptively spatially compressed. The adaptivity in the spectral transform is dependent upon the spectral characteristics (spectral correlation) and the importance of the band. Correlation is very high between most bands of hyperspectral data, which suggests a large amount of redundant information. The bands with less correlation indicate either a significant amount of non-redundant information or poor signal-to-noise characteristics. These spectral characteristics have been shown to be very dependent on the imaging system and atmospheric conditions of the hyperspectral image. The importance of any given band is dependent upon the user's needs, exploitation task and the imaging system. This leads to a spatial compression technique that is selected dependent upon the expected spatial correlation.

Paper Details

Date Published: 6 November 1996
PDF: 9 pages
Proc. SPIE 2821, Hyperspectral Remote Sensing and Applications, (6 November 1996); doi: 10.1117/12.257184
Show Author Affiliations
Bernard V. Brower, Eastman Kodak Co. (United States)
David H. Hadcock, Eastman Kodak Co. (United States)
Joseph P. Reitz, Eastman Kodak Co. (United States)
John R. Schott, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 2821:
Hyperspectral Remote Sensing and Applications
Sylvia S. Shen, Editor(s)

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