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Proceedings Paper

Hyperspectral data compression study: defining the roadmap for data downlink and redistribution
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Paper Abstract

Given the unprecedented volume of data (>72 Megabits per second) that will be generated by the future NOAA Geostationary Operational Environmental Satellite (GOES-R and beyond), the use of innovative data compression techniques will be essential if continuous downlink and re-broadcast from geo-orbit are to be economically feasible. A team of scientists and engineers from the Cooperative Institute for Meteorological Satellite Studies (CIMSS) of the University of Wisconsin-Madison, Offices of Research and Applications and Systems Development of NOAA/NESDIS, NASA/GSFC, and The Aerospace Corporation (a Federally Funded Research and Development Center) has been assembled to study the development of data compression for the next generation GOES sounder. This study is intended to define some feasible approaches for achieving both on-board (lossless) and ground-based (lossy or lossless) data compression. In general, on-board systems have substantially limited processing and storage capabilities, and modest compression ratios, compared with those of ground-based systems. Both highly efficient lossless and lossy algorithms therefore need to be developed to meet both on-board and ground processing objectives. In particular, innovative compression techniques for optimal quantization, transformation, coding, and decoding in interferogram or spectral domains will be essential for practical NOAA real-time operational data processing and distribution. In this presentation we will clearly define testing data sets (real and simulated), approaches, performance and feasibility of achieving hyperspectral data compression to provide a manageable data rate.

Paper Details

Date Published: 16 June 2003
PDF: 8 pages
Proc. SPIE 4897, Multispectral and Hyperspectral Remote Sensing Instruments and Applications, (16 June 2003); doi: 10.1117/12.467589
Show Author Affiliations
Hung-Lung Huang, Univ. of Wisconsin/Madison (United States)
Bormin Huang, Univ. of Wisconsin/Madison (United States)
Timothy J. Schmit, NOAA NESDIS (United States)
Roger Heymann, NOAA NESDIS (United States)

Published in SPIE Proceedings Vol. 4897:
Multispectral and Hyperspectral Remote Sensing Instruments and Applications
Allen M. Larar; Qingxi Tong; Makoto Suzuki, Editor(s)

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