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- W1981657712 abstract "Noise reduction is a crucial step in hyperspectral imagery pre-processing. Based on sensor characteristics, the noise ofhyperspectral imagery represents in both spatial and spectral domain. However, most prevailing denosing techniquesprocess 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 leastsquares filtering techniques. First, in the spatial domain, a new stationary wavelet shrinking algorithm with improvedthreshold function is utilized to adjust the noise level band-by-band. This new algorithm uses BayesShrink for thresholdestimation, and amends the traditional soft-threshold function by adding shape tuning parameters. Comparing with softor hard threshold function, the improved one, which is first-order derivable and has a smooth transitional region betweennoise and signal, could save more details of image edge and weaken Pseudo-Gibbs. Then, in the spectral domain, cubicSavitzky-Golay filter based on least squares method is used to remove spectral noise and artificial noise that may havebeen introduced in during the spatial denoising. Appropriately selecting the filter window width according to priorknowledge, 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 resultshows that the new spatial-spectral denoising algorithm provides more significant signal-to-noise-ratio improvement thantraditional spatial or spectral method, while saves the local spectral absorption features better." @default.
- W1981657712 created "2016-06-24" @default.
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- W1981657712 date "2009-11-04" @default.
- W1981657712 modified "2023-09-27" @default.
- W1981657712 title "A wavelet and least square filter based spatial-spectral denoising approach of hyperspectral imagery" @default.
- W1981657712 doi "https://doi.org/10.1117/12.838096" @default.
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