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Low-complexity adaptive lossless compression of hyperspectral imagery

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dc.contributor.author Klimesh, Matthew
dc.date.accessioned 2008-05-02T16:41:33Z
dc.date.available 2008-05-02T16:41:33Z
dc.date.issued 2006-08-13
dc.identifier.citation SPIE Symposium on Optics and Photonics, San Diego, California, August 13-17, 2006. en_US
dc.identifier.clearanceno 06-2266
dc.identifier.uri http://hdl.handle.net/2014/40772
dc.description.abstract A low-complexity, adaptive predictive technique for lossless compression of hyperspectral imagery is described. This technique is designed to be suitable for implementation in hardware such as a field programmable gate array (FPGA); such an implementation could be used for high-speed compression of hyperspectral imagery onboard a spacecraft. The predictive step of the technique makes use of the sign algorithm, which is a relative of the least mean square (LMS) algorithm from the field of low-complexity adaptive filtering. The compressed data stream consists of prediction residuals encoded using a method similar to that of the JPEG-LS lossless image compression standard. Compression results are presented for several datasets including some raw Airborne Visible/ Infrared Imaging Spectrometer (AVIRIS) datasets and raw Atmospheric Infrared Sounder (AIRS) datasets. The compression effectiveness obtained with the technique is competitive with that of the best of previously described techniques with similar complexity. en_US
dc.description.sponsorship NASA/JPL en_US
dc.language.iso en_US en_US
dc.publisher Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2006. en_US
dc.subject multispectral en_US
dc.subject hyperspectral en_US
dc.subject lossless data compression en_US
dc.title Low-complexity adaptive lossless compression of hyperspectral imagery en_US
dc.type Preprint en_US


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