Abstract: Band selection is an important method of dimensionality reduction of hyperspectral images. In order to reduce the dimensionality, an unsupervised band selection algorithm combining K-L divergence and mutual information was proposed. And theoretical analysis and experimental verification were carried out. Firstly, the band with the largest information entropy was selected as the initial band. Then, the ratio of divergence to mutual information was defined as the criterion of joint K-L divergence mutual information (KLMI). The band which has large KLMI and information entropy was selected to band subset. Then bands with large information and low similarity were obtained. Finally, the real hyperspectral data classification experiments based maximum-variance principle component analysis (MVPCA), affinity propagation (AP), mutual information (MI) and the proposed method were realized by using k-nearest neighbor classifier. Experimental results show that, the accuracy of the proposed algorithm is higher than that of other algorithms. The overall classification accuracy and kappa coefficient κ are over 0.8. Classification accuracy of the most objects is improved. The proposed method has outstanding performance on classification and is a practical dimensionality reduction algorithm of hyperspectral image.