Most hyperspectral remote sensing images suffer from degradation because of the distortion of atmospheric transmission

the limitation of electron devices

and the influence of poor illumination. As a result

the performance of these images in subsequent applications is seriously affected. Thus

the noise in hyperspectral images must be estimated. Given that the noise levels in different bands are often not equivalent in practice

the noise level in each band must be estimated to select an efficient subset of bands. To achieve this end

this paper proposes a hyperspectral image noise estimation algorithm. First

given the high correlation between hyperspectral channels

a low-rank-based model is established specifically for the hyperspectral case. A proper furthermore rule is selected for the noise estimation model to achieve robust performance. Second

the noise in hyperspectral channels is estimated simultaneously using the proposed model. Third

the noise density in each band is calculated as noise level

and the useless bands can be rejected. Experiments are performed on both simulated and real datasets. The proposed method is more robust and can achieve better results than several existing methods because it fully utilizes the correlation and difference between bands. Given that the noise level in bands may be unbalanced

this paper proposes a noise estimation algorithm for hyperspectral images by exploiting the low-rank characteristic of hyperspectral data. By considering noise analysis

this paper proposes a new method to evaluate the quality of hyperspectral images without reference. The proposed algorithm can be applied to highly correlated multichannel images

and the evaluation results are in accordance with expert knowledge and manual interpretation.