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Proceedings Paper

An analysis of the information dependence between MODIS emissive bands
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Paper Abstract

Multispectral, hyperspectral and ultraspectral imagers and sounders are increasingly important for atmospheric science and weather forecasting. The recent advent of multipsectral and hyperspectral sensors measuring radiances in the emissive IR are providing valuable new information. This is due to the presence of spectral channels (in some cases micro-channels) which are carefully positioned in and out of absorption lines of CO2, ozone, and water vapor. These spectral bands are used for measuring surface/cloud temperature, atmospheric temperature, Cirrus clouds water vapor, cloud properties/ozone, and cloud top altidude etc. The complexity of the spectral structure wherein the emissive bands have been selected presents challenges for lossless data compression; these are qualitatively different than the challenges offered by the reflective bands. For a hyperspectral sounder such as AIRS, the large number of channels is the principal contributor to data size. We have shown that methods combining clustering and linear models in the spectral channels can be effective for lossless data compression. However, when the number of emissive channels is relatively small compared to the spatial resolution, such as with the 17 emissive channels of MODIS, such techniques are not effective. In previous work the CCNY-NOAA compression group has reported an algorithm which addresses this case by sequential prediction of the spatial image. While that algorithm demonstrated an improved compression ratio over pure JPEG2000 compression, it underperformed optimal compression ratios estimated from entropy. In order to effectively exploit the redundant information in a progressive prediction scheme we must, determine a sequence of bands in which each band has sufficient mutual information with the next band, so that it predicts it well. We will provide a covariance and mutual information based analysis of the pairwise dependence between the bands and compare this with the qualitative expected dependence suggested by a physical analysis. This compression research is managed by Roger Heymann, PE of OSD NOAA NESDIS Engineering, in collaboration with the NOAA NESDIS STAR Research Office through Mitch Goldberg, Tim Schmit, Walter Wolf.

Paper Details

Date Published: 5 September 2008
PDF: 11 pages
Proc. SPIE 7084, Satellite Data Compression, Communication, and Processing IV, 70840J (5 September 2008); doi: 10.1117/12.800817
Show Author Affiliations
Srikanth Gottipati, CCNY, NOAA/CREST (United States)
Irina Gladkova, CCNY, NOAA/CREST (United States)
Michael Grossberg, CCNY, NOAA/CREST (United States)


Published in SPIE Proceedings Vol. 7084:
Satellite Data Compression, Communication, and Processing IV
Bormin Huang; Roger W. Heymann; Joan Serra-Sagristà, Editor(s)

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