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Proceedings Paper

Wavelet threshold denoising for hyperspectral data in spectral domain
Author(s): Lili Jiang; Xiaomei Chen; Guoqiang Ni; Shule Ge
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Paper Abstract

An improved method of wavelet threshold denoising is introduced and applied to hyperspectral imagery denoising in spectral domain. This method estimates a threshold value for each spectrum. Thresholds are set to a scalar specifying the percentage of cumulative power to retain in the filtered wavelet transform. Find the actual percent corresponding to these coefficients. During the processing, four families of mother wavelets (Symlets, Daubechies, Haar and Coiflet) are tested in a series of experiments to estimate the functioning of those wavelets and thresholding parameters. Experimental results show that the proposed algorithm with Coiflet provides an improvement in SNR for hyperspectral data specially.

Paper Details

Date Published: 29 December 2008
PDF: 6 pages
Proc. SPIE 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA), 728519 (29 December 2008); doi: 10.1117/12.815808
Show Author Affiliations
Lili Jiang, Beijing Institute of Technology (China)
Xiaomei Chen, Beijing Institute of Technology (China)
Guoqiang Ni, Beijing Institute of Technology (China)
Shule Ge, Beijing Institute of Technology (China)

Published in SPIE Proceedings Vol. 7285:
International Conference on Earth Observation Data Processing and Analysis (ICEODPA)
Deren Li; Jianya Gong; Huayi Wu, Editor(s)

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