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	Noise reduction of hyperspectral data based on rudin-osher-fatemi model
	Dong, Yingying ; Wang, Jihua ; Zhu, Yining ; Yang, Guijun
	2014
英文摘要	Aiming to reduce the noise of hyperspectral data, the Rudin-Osher-Fatemi (ROF) model is selected to quantitatively describe the statistical distribution characteristics of noise data, and the Chambolle's algorithm is chosen for ROF model numerical solving to achieve denoised hyperspectral data. The key point of using ROF model for data denoising is setting its filtering parameters. In this study, we proposed an automatic parameters selection method for ROF model. Winter wheat was taken as the experimental object, and the performance of ROF model in noise reduction was verified on two datasets, i.e., hyperspectral canopy reflectance dataset simulated with PROSAIL (PROSPECT (Leaf Optical Property Model) + SAIL (Scattering by Arbitrarily Inclined Leaves)) model, and ground hyperspectral canopy reflectance dataset measured in field experiments. Numerical results demonstrated the effectiveness of the ROF model in reducing noise of hyperspectral data, as well as keeping the important features of reflectance spectra. Also, the results indicated that the denoised hyperspectral data based on the ROF model were in better quality, compared to the denoised hyperspectral data based on moving average filtering algorithm, Savitzky-Golay filtering algorithm, and wavelet denoising algorithm. Copyright ? 2014 American Scientific Publishers.; EI; 0
语种	英语
出处	EI
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/412652]
专题	数学科学学院
推荐引用方式 GB/T 7714	Dong, Yingying,Wang, Jihua,Zhu, Yining,et al. Noise reduction of hyperspectral data based on rudin-osher-fatemi model. 2014-01-01.

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