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Proceedings Paper

On empirical mode decomposition for ultraspectral sounder data compression
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Paper Abstract

A method combining the empirical mode decomposition (EMD) and the principal component analysis (PCA) was recently proposed for lossless compression of ultraspectral sounder data. In that method, data residual is obtained via the linear regression of the data against m intrinsic mode functions (IMFs) which are obtained from the EMD of the data mean, followed by the linear regression of the IMF regression error against a truncated number, n, of their corresponding principal components (PCs). In this paper we show that this two-stage (m IMFs + n PCs) linear transform approach is not as good as its counterpart two-stage (m PCs + n PCs) linear transform approach in terms of data residual and compression ratio of ultraspectral data, given the same number of IMFs and PCs used respectively at the first stage, followed by the same number of PCs used at the second stage. Mathematically, the two-stage (m PCs + n PCs) linear transform approach is equivalent to a single linear transform with (m + n) PCs. In other words, the simple PCA compression method outperforms this combined EMD and PCA compression method. This is expected because the PCA (also called the Karhunen-Loève transform or the Hotelling transform) is known to be the optimal linear transform in the sense of minimizing the mean squared error.

Paper Details

Date Published: 13 September 2005
PDF: 7 pages
Proc. SPIE 5889, Satellite Data Compression, Communications, and Archiving, 58890L (13 September 2005); doi: 10.1117/12.621414
Show Author Affiliations
Bormin Huang, CIMSS, Univ. of Wisconsin-Madison (United States)
Alok Ahuja, CIMSS, Univ. of Wisconsin-Madison (United States)
Hung-Lung Huang, CIMSS, Univ. of Wisconsin-Madison (United States)


Published in SPIE Proceedings Vol. 5889:
Satellite Data Compression, Communications, and Archiving
Bormin Huang; Roger W. Heymann; Charles C. Wang, Editor(s)

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