Hyperspectral lossless compression

Details Hyperspectral lossless compression Hyperspectral lossless compression

Oct 1999

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1999spie.3753..247b&link_type=abstract

Proc. SPIE Vol. 3753, p. 247-257, Imaging Spectrometry V, Michael R. Descour; Sylvia S. Shen; Eds.

Computer Science

Scientific paper

Hyperspectral image data presents challenges to current transmission bandwidth and storage capabilities. To overcome these challenges and to retain the radiometric accuracy of the data, there is a need for good hyperspectral lossless compression. The current state-of-the-art lossless compression algorithm is JPEG-LS, which uses a 2-D edge-detecting predictor. Hyperspectral systems sample the electromagnetic spectrum very finely, which results in increased spectral correlation. A predictor that takes into account previous band information can obtain substantial gains in compression ratio. This paper discusses a number of different predictors that take advantage of the significant band-to-band (spectral) correlation within the hyperspectral imagery. A sample set of HYDICE, AVIRIS, and SEBASS imagery was used to evaluate the different predictors. While the JPEG-LS algorithm achieved just greater than 2:1 on most imagery, some of the 3-D prediction techniques achieved greater than 3:1 compression ratio. The characteristics of these test images and results from different predictors are presented in this paper.

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