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Proceedings Paper

Data compression studies for NOAA Hyperspectral Environmental Suite (HES) using 3D integer wavelet transforms with 3D set partitioning in hierarchical trees
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Paper Abstract

The next-generation NOAA/NESDIS GOES-R hyperspectral sounder, now referred to as the HES (Hyperspectral Environmental Suite), will have hyperspectral resolution (over one thousand channels with spectral widths on the order of 0.5 wavenumber) and high spatial resolution (less than 10 km). Hyperspectral sounder data is a particular class of data requiring high accuracy for useful retrieval of atmospheric temperature and moisture profiles, surface characteristics, cloud properties, and trace gas information. Hence compression of these data sets is better to be lossless or near lossless. Given the large volume of three-dimensional hyperspectral sounder data that will be generated by the HES instrument, the use of robust data compression techniques will be beneficial to data transfer and archive. In this paper, we study lossless data compression for the HES using 3D integer wavelet transforms via the lifting schemes. The wavelet coefficients are processed with the 3D set partitioning in hierarchical trees (SPIHT) scheme followed by context-based arithmetic coding. SPIHT provides better coding efficiency than Shapiro's original embedded zerotree wavelet (EZW) algorithm. We extend the 3D SPIHT scheme to take on any size of 3D satellite data, each of whose dimensions need not be divisible by 2N, where N is the levels of the wavelet decomposition being performed. The compression ratios of various kinds of wavelet transforms are presented along with a comparison with the JPEG2000 codec.

Paper Details

Date Published: 5 February 2004
PDF: 11 pages
Proc. SPIE 5238, Image and Signal Processing for Remote Sensing IX, (5 February 2004); doi: 10.1117/12.511437
Show Author Affiliations
Bormin Huang, Univ. of Wisconsin/Madison (United States)
Hung-Lung Huang, Univ. of Wisconsin/Madison (United States)
Hao Chen, Univ. of Wisconsin/Madison (United States)
Alok Ahuja, Univ. of Wisconsin/Madison (United States)
Kevin Baggett, Univ. of Wisconsin/Madison (United States)
Timothy J. Schmit, National Oceanic and Atmospheric Administration (United States)
Roger W. Heymann, National Oceanic and Atmospheric Administration (United States)


Published in SPIE Proceedings Vol. 5238:
Image and Signal Processing for Remote Sensing IX
Lorenzo Bruzzone, Editor(s)

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