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Please use this identifier to cite or link to this item: http://hdl.handle.net/2014/40772

Title: Low-complexity adaptive lossless compression of hyperspectral imagery
Authors: Klimesh, Matthew
Keywords: multispectral
hyperspectral
lossless data compression
Issue Date: 13-Aug-2006
Publisher: Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2006.
Citation: SPIE Symposium on Optics and Photonics, San Diego, California, August 13-17, 2006.
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.
URI: http://hdl.handle.net/2014/40772
Appears in Collections:JPL TRS 1992+

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