|
Multispectral imaging is becoming an increasingly important tool for monitoring the earth and its environment
from space borne and airborne platforms. Multispectral imaging data consists of visible and IR measurements
from a scene across space and spectrum. Growing data rates resulting from faster scanning and finer spatial and
spectral resolution makes compression an increasingly critical tool to reduce data volume for transmission and
archiving. Research for NOAA NESDIS has been directed to finding for the characteristics of satellite atmospheric
Earth science Imager sensor data what level of Lossless compression ratio can be obtained as well as appropriate
types of mathematics and approaches that can lead to approaching this data's entropy level. Conventional
lossless do not achieve the theoretical limits for lossless compression on imager data as estimated from the
Shannon entropy. In a previous paper, the authors introduce a lossless compression algorithm developed for
MODIS as a proxy for future NOAA-NESDIS satellite based Earth science multispectral imagers such as GOES-R.
The algorithm is based on capturing spectral correlations using spectral prediction, and spatial correlations
with a linear transform encoder. In decompression, the algorithm uses a statistically computed look up table to
iteratively predict each channel from a channel decompressed in the previous iteration. In this paper we present
a new approach which fundamentally differs from our prior work. In this new approach, instead of having a
single predictor for each pair of bands we introduce a piecewise spatially varying predictor which significantly
improves the compression results. Our new algorithm also now optimizes the sequence of channels we use for
prediction. Our results are evaluated by comparison with a state of the art wavelet based image compression
scheme, Jpeg2000. We present results on the 14 channel subset of the MODIS imager, which serves as a proxy
for the GOES-R imager. We will also show results of the algorithm for on NOAA AVHRR data and data from
SEVIRI. The algorithm is designed to be adapted to the wide range of multispectral imagers and should facilitate
distribution of data throughout globally. 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.
|
