WSEAS Transactions on Signal Processing
Print ISSN: 1790-5052, E-ISSN: 2224-3488
Volume 15, 2019
Robust Denoising Method based on Tensor Models Decomposition for Hyperspectral Imagery
Authors: ,
Abstract: In the hyperspectral images (HSI) acquired by the new-generation hyperspectral sensors the signal dependent noise is an important limitation to the detection or classification. Therefore, noise reduction is an important preprocessing step to analyze the information in the hyperspectral image (HSI). A signal dependent noise cannot be reduced by conventional linear filtering. Therefore, a new method based on multiple linear regression (MLR) and Parallel factor analysis (PARAFAC) decomposition is proposed to estimate the noise of hyperspectral remote sensing image. Then, the estimated noise is used for whitening the colored structural noise. By using this transformation, the data noise from new-generation hyperspectral sensors is diminished, thereby allowing a minimization of negative impacts on hyperspectral detection and classification applications.
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Pages: 20-29
WSEAS Transactions on Signal Processing, ISSN / E-ISSN: 1790-5052 / 2224-3488, Volume 15, 2019, Art. #4