Dual-clustering-based hyperspectral band selection by contextual analysis

Hyperspectral image (HSI) involves vast quantities of information that can help with the image analysis. However, this information has sometimes been proved to be redundant, considering specific applications such as HSI classification and anomaly detection. To address this problem, hyperspectral band selection is viewed as an effective dimensionality reduction method that can remove the redundant components of HSI. Various HSI band selection methods have been proposed recently, and the clustering-based method is a traditional one. This agglomerative method has been considered simple and straightforward, while the performance is generally inferior to the state of the art. To tackle the inherent drawbacks of the clustering-based band selection method, a new framework concerning on dual clustering is proposed in this paper. The main contribution can be concluded as follows: 1) a novel descriptor that reveals the context of HSI efficiently; 2) a dual clustering method that includes the contextual information in the clustering process; 3) a new strategy that selects the cluster representatives jointly considering the mutual effects of each cluster. Experimental results on three real-world HSIs verify the noticeable accuracy of the proposed method, with regard to the HSI classification application. The main comparison has been conducted among several recent clustering-based band selection methods and constraint-based band selection methods, demonstrating the superiority of the technique that we present.