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Algorithm for Lossless Compression of Calibrated Hyperspectral ImageryA two-stage predictive method was developed for lossless compression of calibrated hyperspectral imagery. The first prediction stage uses a conventional linear predictor intended to exploit spatial and/or spectral dependencies in the data. The compressor tabulates counts of the past values of the difference between this initial prediction and the actual sample value. To form the ultimate predicted value, in the second stage, these counts are combined with an adaptively updated weight function intended to capture information about data regularities introduced by the calibration process. Finally, prediction residuals are losslessly encoded using adaptive arithmetic coding. Algorithms of this type are commonly tested on a readily available collection of images from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral imager. On the standard calibrated AVIRIS hyperspectral images that are most widely used for compression benchmarking, the new compressor provides more than 0.5 bits/sample improvement over the previous best compression results. The algorithm has been implemented in Mathematica. The compression algorithm was demonstrated as beneficial on 12-bit calibrated AVIRIS images.
Document ID
Document Type
Other - NASA Tech Brief
Kiely, Aaron B. (California Inst. of Tech. Pasadena, CA, United States)
Klimesh, Matthew A. (California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
August 25, 2013
Publication Date
March 1, 2010
Publication Information
Publication: NASA Tech Briefs, March 2010
Subject Category
Man/System Technology and Life Support
Report/Patent Number
Distribution Limits
Public Use Permitted.

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NameType 20100009675.pdf STI

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IDRelationTitle20100009649Analytic PrimaryNASA Tech Briefs, March 2010