It often plays a key role to extract homogeneous regions in the existing noise estimating methods for hyperspectral images (HSI). An effective homogeneous region detection method can improve the accuracy of image noise estimation. An isotropic homogeneous region detection algorithm (IHRDA) is proposed by using spatial information and spectral information, where a new Lance-SAD metric (LSM) is constructed to distinguish the similarity of picture elements in the homogeneous regions; then the noise level of hyperspectral images is estimated using the optimal regions with decorrelation based on multivariable linear regression (MLR) model. In experiments, synthetic images with different structure under different signal to noise ratio (SNR) and true hyperspectral remote sensing images are both compared with many existing methods, which show that the proposed method is more accurate and stable for hyperspectral images with various complexities and different noise levels.