A Divisive Hierarchical Clustering Approach to Hyperspectral Band Selection

Abstract

Band selection, considered as an effective dimensionality reduction technique for hyperspectral imagery (HSI), has become a hot topic for decades. Although various clustering-based methods have been applied to band selection, only a few studies explored the hierarchical structure among different spectral bands. And with regard to conventional hierarchical clustering, implemented in an agglomerative manner, both efficiency and accuracy of band selection still remain to rise. Moreover, the noise sensitivity is a defect inherent in the procedure of clustering. To address these issues, we propose a divisive hierarchical clustering approach (DHCA) to hyperspectral band selection. Inspired by divisive analysis, DHCA is designed to obtain any number of band subsets, which captures the intrinsic hierarchy of hyperspectral bands simultaneously. By introducing the local density into average dissimilarity, it can suppress the outliers clustering separately. Also, given the order of the spectrum, channel interval makes the similarity more rational among bands. Finally, we select a representative band in each cluster from the information viewpoint to ensure the band subset with a high quality. Extensive experiments on three real public HSI datasets fully validate the superiority of the proposed method against state-of-the-art competitors.