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Proceedings Paper

Hyperspectral data compression using a Wiener filter predictor
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Paper Abstract

The application of compression to hyperspectral image data is a significant technical challenge. A primary bottleneck in disseminating data products to the tactical user community is the limited communication bandwidth between the airborne sensor and the ground station receiver. This report summarizes the newly-developed “Z-Chrome” algorithm for lossless compression of hyperspectral image data. A Wiener filter prediction framework is used as a basis for modeling new image bands from already-encoded bands. The resulting residual errors are then compressed using available state-of-the-art lossless image compression functions. Compression performance is demonstrated using a large number of test data collected over a wide variety of scene content from six different airborne and spaceborne sensors .

Paper Details

Date Published: 24 September 2013
PDF: 11 pages
Proc. SPIE 8871, Satellite Data Compression, Communications, and Processing IX, 887102 (24 September 2013); doi: 10.1117/12.2024629
Show Author Affiliations
Pierre V. Villeneuve, Space Computer Corp. (United States)
Scott G. Beaven, Space Computer Corp. (United States)
Alan D. Stocker, Space Computer Corp. (United States)


Published in SPIE Proceedings Vol. 8871:
Satellite Data Compression, Communications, and Processing IX
Bormin Huang; Antonio J. Plaza; Chein-I Chang, Editor(s)

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