Obtaining accurate noise estimation in texture-rich hyperspectral images is difficult in the noise estimation task. A spectral decorrelation method based on the spatial regularity and spectral correlation of hyperspectral images is described in this paper. Homogenous region division is a key step in many noise estimation methods， and a precise homogeneous region division can effectively improve the accuracy of noise estimation. To this end， a simple linear iterative clustering algorithm is combined with spectral-spatial similarity to segment hyperspectral images into locally structured similar image blocks to maintain homogeneous features. Spectral information divergence and spectral angle are combined as the spectral distance measurement to improve the ability of discrimination between spectra. Spectral correlations are removed within homogeneous regions by multiple linear regression to obtain the noise levels of the residual images. Various degrees of noise are added to simulated images of varying ground complexity， and the effectiveness and stability of this method are verified by comparison with a variety of methods. Finally， the proposed method is successfully applied to noise level estimation of Urban data， and can accurately identify bands heavily polluted by noise.