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
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.