An Unsupervised Hyperspectral Band Selection Method Based on Shared Nearest Neighbor and Correlation Analysis

Band selection is an important dimensionality reduction (DR) methodology for hyperspectral images (HSI). In recent years, many ranking-based clustering band selection methods have been developed. However, these methods do not consider the combination of bands in different clusters but only select the desired number of clustering centers based on band ranking to construct the reduced band subset, which may lead to obtaining a set of bands with low redundancy but little information or a set of bands with a large amount of information but high redundancy, thus falling into the local optimal solution set. To solve this problem, an unsupervised hyperspectral band selection method based on shared nearest neighbor and correlation analysis (SNNCA) is proposed in this paper. The proposed SNNCA method considers the interaction of bands in different clusters, and can obtain a set of bands with a large amount of information and low redundancy. First, this method uses the shared nearest neighbor to describe the local density of each band and takes the product of local density and distance factor as the weight to rank each band to select the required number of clustering centers, which ensures low redundancy among the clustering centers. Then, all bands are grouped into several clusters based on the Euclidean distance matrix and the clustering centers. Finally, the correlation among intra-cluster and inter-cluster bands and the information entropy are further analyzed, and the most representative band is selected from each cluster. The experimental results on two HSI datasets demonstrate that the proposed SNNCA method achieves better classification performance than that of other state-of-the-art comparison methods and possesses competitive running time.