Compression of AIRS data using Emperical Mode Decomposition

Irina Gladkova; Leonid Roytman; Mitchell D. Goldberg; John Weber

doi:10.1117/12.558967

14 October 2004 Compression of AIRS data using Emperical Mode Decomposition

Irina Gladkova, Leonid Roytman, Mitchell D. Goldberg, John Weber

Proceedings Volume 5548, Atmospheric and Environmental Remote Sensing Data Processing and Utilization: an End-to-End System Perspective; (2004) https://doi.org/10.1117/12.558967
Event: Optical Science and Technology, the SPIE 49th Annual Meeting, 2004, Denver, Colorado, United States

Abstract

In this paper, we consider an application of the Empirical Mode Decomposition (EMD) introduced by Norden E. Huang in 1996 to the compression of 3D hyperspectral sounding data. The EMD is a new data analysis method which is based on expansion of the data in terms of Intrinsic Mode Functions (IMF). These IMFs are based on and derived from the data set. Since EMD adaptively represent the signal as a sum of "well behaved" amplitude/frequency modulated components, we found it very well suited for the whitening part of the compression scheme.

Citation Download Citation

Irina Gladkova, Leonid Roytman, Mitchell D. Goldberg, and John Weber "Compression of AIRS data using Emperical Mode Decomposition", Proc. SPIE 5548, Atmospheric and Environmental Remote Sensing Data Processing and Utilization: an End-to-End System Perspective, (14 October 2004); https://doi.org/10.1117/12.558967

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