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Proceedings Paper

Vector quantization with reversible variable-length coding for ultra-spectral sounder data compression: an application to future NOAA weather satellite data rebroadcast
Author(s): Bormin Huang; Shih-Chieh Wei
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Paper Abstract

Contemporary and future ultraspectral sounders represent significant technical advancement for environmental and meteorological prediction and monitoring. Given their large volume of spectral observations, the use of robust data compression techniques will be beneficial to data transmission and storage. The retrieval of geophysical parameters from ultraspectral sounder observation via the radiative transfer equation is a mathematically ill-posed problem. Lossless compression of ultraspectral sounder data is desired by the science community to avoid potential retrieval degradation. For NOAA's future geostationary weather satellites, the data is managed to be transmitted down to the ground within the bandwidth capabilities of the satellite transmitter and ground station receiving system. The data is then compressed at the ground station for distribution to the user community, as is traditionally performed with the GOES data via satellite rebroadcast. In this paper we investigate a lossless compression method with fast precomputed vector quantization (FPVQ) and reversible variable-length coding (RVLC). The FPVQ produces high compression gain for ground operation while RVLC affords better detection of bit errors remaining after channel decoding due to synchronization losses over a noisy channel. The FPVQ-RVLC compression method provides a good tool for satellite rebroadcast of ultraspectral sounder data.

Paper Details

Date Published: 15 November 2007
PDF: 9 pages
Proc. SPIE 6787, MIPPR 2007: Multispectral Image Processing, 67872B (15 November 2007); doi: 10.1117/12.752241
Show Author Affiliations
Bormin Huang, Univ. of Wisconsin, Madison (United States)
Shih-Chieh Wei, Univ. of Wisconsin, Madison (United States)


Published in SPIE Proceedings Vol. 6787:
MIPPR 2007: Multispectral Image Processing

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