1 September 2004 Lossless compression of three-dimensional hyperspectral sounder data using context-based adaptive lossless image codec with bias-adjusted reordering
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Abstract
Hyperspectral sounder data is used for retrieval of atmospheric temperature, moisture and trace gas profiles, surface temperature and emissivity, and cloud and aerosol optical properties. This large volume of data is 3-D in nature with many scan lines containing cross-track footprints, each with thousands of IR channels. Unlike hyperspectral imager data compression, hyperspectral sounder data compression is desired to be lossless or near-lossless to avoid substantial degradation of the geophysical retrieval. For this new class of data for compression studies, a lossless compression algorithm combining the context-based adaptive lossless image codec (CALIC) and a novel bias-adjusted reordering (BAR) scheme is presented. The 3-D data are arranged into two dimensions with the original 2-D spatial domain converted into one dimension using a continuous scan order. In the BAR scheme, the data are reordered such that the bias-adjusted distance between any two neighboring vectors is minimized. The result is then encoded using the CALIC algorithm with significant compression gains over using the CALIC algorithm alone.
© (2004) Society of Photo-Optical Instrumentation Engineers (SPIE)
Bormin Huang, Alok Ahuja, Hung-Lung Huang, Timothy J. Schmit, Roger W. Heymann, "Lossless compression of three-dimensional hyperspectral sounder data using context-based adaptive lossless image codec with bias-adjusted reordering," Optical Engineering 43(9), (1 September 2004). https://doi.org/10.1117/1.1778732 . Submission:
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