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Proceedings Paper

Lossless data compression for infrared hyperspectral sounders: an update
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Paper Abstract

The compression of hyperspectral sounder data is beneficial for more efficient archive and transfer given its large 3-D volume. Moreover, since physical retrieval of geophysical parameters from hyperspectral sounder data is a mathematically ill-posed problem that is sensitive to the error of the data, lossless or near-lossless compression is desired. This paper provides an update into applications of state-of-the-art 2D and 3D lossless compression algorithms such as 3D EZW, 3D SPIHT, 2D JPEG2000, 2D JPEG-LS and 2D CALIC for hyperspectral sounder data. In addition, in order to better explore the correlations between the remote spectral regions affected by the same type of atmospheric absorbing constituents or clouds, the Bias-Adjusted Reordering (BAR) scheme is presented which reorders the data such that the bias-adjusted distance between any two neighboring vectors is minimized. This scheme coupled with any of the state-of-the-art compression algorithms produces significant compression gains.

Paper Details

Date Published: 14 October 2004
PDF: 11 pages
Proc. SPIE 5548, Atmospheric and Environmental Remote Sensing Data Processing and Utilization: an End-to-End System Perspective, (14 October 2004); doi: 10.1117/12.560404
Show Author Affiliations
Bormin Huang, Univ. of Wisconsin/Madison (United States)
Hung-Lung Allen Huang, Univ. of Wisconsin/Madison (United States)
Alok Ahuja, Univ. of Wisconsin/Madison (United States)
Timothy J. Schmit, National Oceanic and Atmospheric Administration/NESDIS (United States)
Roger W. Heymann, National Oceanic and Atmospheric Administration/NESDIS (United States)


Published in SPIE Proceedings Vol. 5548:
Atmospheric and Environmental Remote Sensing Data Processing and Utilization: an End-to-End System Perspective
Hung-Lung Allen Huang; Hal J. Bloom, Editor(s)

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