Compression of hyper/ultraspectral data

Two data compression algorithms intended for the compression of hyper and ultraspectral data are reviewed. These methods have been successfully applied to the compression of NASA JPL AVIRIS hyperspectral images. The two algorithms are based on slightly different requirements and assumptions. The first one is a low complexity, real-time, inter-band, least squares optimized predictor (SLSQ) whose raster-scan nature makes it amenable for on-board implementation. The second is a partitioned vector quantization algorithm (LPVQ) with tunable quality ranging from lossless to lossy. LPVQ is more complex, but it allows fast browsing and pure-pixel classification in the compressed domain, so it is more suitable to archival and distribution of compressed data. Both approaches compare well to the state-of-the-art in the compression of AVIRIS data. Preliminary results on the compression of AIRS ultraspectral sounder data are presented.

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