Band Selection Based on Band Clustering for Hyperspectral Imagery

In order to preserve the information of hyperspectral imagery more effectively,an unsupervised band selection algorithm for hyperspectral imagery based on band clustering is proposed in this paper.Firstly,mutual information between every two bands is calculated to measure the degree of correlation.Then,clustering within bands is realized by calculating the mutual information.After iterative computation,the bands within the same class are clustered around the most important bands automatically,and the clustering operation stops until the clustering center does not change.As a consequence of clustering,the redundancy bands are removed while the useful information is retained.Finally,the selected band subsets are determined by the clustering centers.Experimental results show that compared with traditional methods,the band set obtained by the proposed method can preserve more spectral information effectively and acquire higher classification accuracy.