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Proceedings Paper

Low-complexity adaptive lossless compression of hyperspectral imagery
Author(s): Matthew Klimesh
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Paper 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.

Paper Details

Date Published: 1 September 2006
PDF: 9 pages
Proc. SPIE 6300, Satellite Data Compression, Communications, and Archiving II, 63000N (1 September 2006); doi: 10.1117/12.682624
Show Author Affiliations
Matthew Klimesh, Jet Propulsion Lab. (United States)


Published in SPIE Proceedings Vol. 6300:
Satellite Data Compression, Communications, and Archiving II
Roger W. Heymann; Charles C. Wang; Timothy J. Schmit, Editor(s)

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