Abstract: In order to remove data redundancy of hyperspectral images, and improve the accuracy and efficiency of hyperspectral image processing, a band selection algorithm was proposed based on band index of hyperspectral images. Wavelet transform was used to deal with the noise of hyperspectral image data. Bands are divided into groups by using joint skewness-kurtosis figure, and the band was removed as a redundant band which was determined based on the size of band index. The set of the minimum bands was obtained in this way. The experimental results show that the endmember set selected by using the above bands is consistent with that selected by using all bands. The redundancy band is removed to the greatest extent without affecting the endmember extraction. The classification accuracy of the band set is close to that of all bands. The band selection algorithm is feasible and effective. The study provides help to reduce the dimension of hyperspectral images.