An adaptive band selection algorithm for dimension reduction of hyperspectral images

An adaptive band selection algorithm for dimension reduction of hyperspectral images is proposed. Considering the spatial correlation and spectral correlation, a selection rule, referring to spectral information and its correlation, is constructed for band selection. To test the efficiency of this algorithm, K-means algorithm for unsupervised classification was applied on images generated from the algorithm. The results showed that the proposed algorithm reduced the computation amount and improved the classification accuracy.