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Hyperspectral image noise reduction based on rank-1 tensor decomposition
2013
ISPRS journal of photogrammetry and remote sensing (Print)
In this study, a novel noise reduction algorithm for hyperspectral imagery (HSI) is proposed based on high-order rank-1 tensor decomposition. The hyperspectral data cube is considered as a three-order tensor that is able to jointly treat both the spatial and spectral modes. Subsequently, the rank-1 tensor decomposition (R1TD) algorithm is applied to the tensor data, which takes into account both the spatial and spectral information of the hyperspectral data cube. A noise-reduced hyperspectral
doi:10.1016/j.isprsjprs.2013.06.001
fatcat:n4xioih5zjhzfhr2tmrfqqgezy