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

Hung-Lung Huang; Bormin Huang; Timothy J. Schmit; Roger Heymann

doi:10.1117/12.467589

16 June 2003 Hyperspectral data compression study: defining the roadmap for data downlink and redistribution

Hung-Lung Huang, Bormin Huang, Timothy J. Schmit, Roger Heymann

Proceedings Volume 4897, Multispectral and Hyperspectral Remote Sensing Instruments and Applications; (2003) https://doi.org/10.1117/12.467589
Event: Third International Asia-Pacific Environmental Remote Sensing Remote Sensing of the Atmosphere, Ocean, Environment, and Space, 2002, Hangzhou, China

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.

Citation Download Citation

Hung-Lung Huang, Bormin Huang, Timothy J. Schmit, and Roger Heymann "Hyperspectral data compression study: defining the roadmap for data downlink and redistribution", Proc. SPIE 4897, Multispectral and Hyperspectral Remote Sensing Instruments and Applications, (16 June 2003); https://doi.org/10.1117/12.467589

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KEYWORDS

Data compression

Image compression

Wavelets

Algorithm development

Wavelet transforms

Electronic filtering

Linear filtering

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