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Proceedings Paper

A wavelet and least square filter based spatial-spectral denoising approach of hyperspectral imagery
Author(s): Ting Li; Xiao-mei Chen; Gang Chen; Bo Xue; Guo-qiang Ni
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Paper Abstract

Noise reduction is a crucial step in hyperspectral imagery pre-processing. Based on sensor characteristics, the noise of hyperspectral imagery represents in both spatial and spectral domain. However, most prevailing denosing techniques process the imagery in only one specific domain, which have not utilized multi-domain nature of hyperspectral imagery. In this paper, a new spatial-spectral noise reduction algorithm is proposed, which is based on wavelet analysis and least squares filtering techniques. First, in the spatial domain, a new stationary wavelet shrinking algorithm with improved threshold function is utilized to adjust the noise level band-by-band. This new algorithm uses BayesShrink for threshold estimation, and amends the traditional soft-threshold function by adding shape tuning parameters. Comparing with soft or hard threshold function, the improved one, which is first-order derivable and has a smooth transitional region between noise and signal, could save more details of image edge and weaken Pseudo-Gibbs. Then, in the spectral domain, cubic Savitzky-Golay filter based on least squares method is used to remove spectral noise and artificial noise that may have been introduced in during the spatial denoising. Appropriately selecting the filter window width according to prior knowledge, this algorithm has effective performance in smoothing the spectral curve. The performance of the new algorithm is experimented on a set of Hyperion imageries acquired in 2007. The result shows that the new spatial-spectral denoising algorithm provides more significant signal-to-noise-ratio improvement than traditional spatial or spectral method, while saves the local spectral absorption features better.

Paper Details

Date Published: 24 November 2009
PDF: 11 pages
Proc. SPIE 7513, 2009 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Process Technology, 75132A (24 November 2009); doi: 10.1117/12.838096
Show Author Affiliations
Ting Li, Beijing Institute of Technology (China)
Xiao-mei Chen, Beijing Institute of Technology (China)
Gang Chen, Beijing Institute of Technology (China)
Bo Xue, Beijing Institute of Technology (China)
Guo-qiang Ni, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 7513:
2009 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Process Technology
Toru Yoshizawa; Ping Wei; Jesse Zheng, Editor(s)

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