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. Examples of multispectral sensors we consider include the NASA 36 band MODIS imager, Meteosat 2nd generation 12 band SEVIRI imager, GOES R series 16 band ABI imager, current generation GOES 5 band imager, and Japan’s 5 band MTSAT imager. Conventional lossless compression algorithms are not able to reach satisfactory compression ratios nor are they near the upper limits for lossless compression on imager data as estimated from the Shannon entropy. We introduce a new lossless compression algorithm developed for the NOAA-NESDIS satellite based Earth science multispectral imagers. The algorithm is based on capturing spectral correlations using spectral prediction, and spatial correlations with a linear transform encoder. The algorithm as presented has been designed to work with NOAA’s scientific data and so is purely lossless but lossy modes can be supported. The compression algorithm also structures the data in a way that makes it easy to incorporate robust error correction using FEC coding methods as TPC and LDPC for satellite use. This research was funded by NOAA-NESDIS for its Earth observing satellite program and NOAA goals. 1
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