The Application of Principal Component Analysis (PCA) to AIRS Data Compression

Lihang Zhou; Mitchell D. Goldberg; Walter W. Wolf; Chris Barnet

doi:10.1364/HISE.2005.HTuD9

Fourier Transform Spectroscopy/ Hyperspectral Imaging and Sounding of the Environment
Technical Digest (CD) (Optica Publishing Group, 2005),
paper HTuD9
•https://doi.org/10.1364/HISE.2005.HTuD9

The Application of Principal Component Analysis (PCA) to AIRS Data Compression

Lihang Zhou, Mitchell D. Goldberg, Walter W. Wolf, and Chris Barnet

Hyperspectral Imaging and Sounding of the Environment 2005

Alexandria, Virginia United States
31 January–3 February 2005
ISBN: 1-55752-780-6

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L. Zhou, M. D. Goldberg, W. W. Wolf, and C. Barnet, "The Application of Principal Component Analysis (PCA) to AIRS Data Compression," in Fourier Transform Spectroscopy/ Hyperspectral Imaging and Sounding of the Environment, Technical Digest (CD) (Optica Publishing Group, 2005), paper HTuD9.

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Abstract

PCA is a powerful tool for AIRS data compression. Eigenvector from a dependent dataset works the best for data compression; while those from an independent dataset can be used to diagnose climate abnormal signals or high noise in the observations.

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