Description

In our proposed reducing random noise in Hyper-spectral images (HIS) both dependent and independent from signal based on Tensor-decomposition methods. Noise is described by a parametric model so the noise variance depends on the signal. We remove the noise both from both signal-dependent (SD) and signal-independent (SI) noise, some hybrid methods, which reduce noise by two steps according to the different statistical properties of those two types of noise, are proposed in this paper. The first one, named as the PARAFACSI–PARAFACSD method, uses a multi linear algebra model, parallel factor analysis (PARAFAC) decomposition, twice to remove SI and SD noise, respectively. The second one is a combination of the multiple-linear-regression-based approach termed as the Hyper-spectral Noise Estimation (HYNE) method and PARAFAC decomposition, which is named as the HYNE-PARAFAC method. The last one combines the multidimensional Multi-dimentional Wiener filter (MWF) method and PARAFAC decomposition and is named as the MWF-PARAFAC method. SI noise is removed from the original image by PARAFAC decomposition, the HYNE method, or the MWF method based on the property of SI Noise SD components can be further reduced by PARAFAC decomposition due to its own statistical property. In take the data set for that process to remove the SI and SD noise in the hyper spectral image.