Unsupervised Hyperspectral Band Selection Based on Maximum Information Entropy and Determinantal Point Process

Abstract

Band selection is of great important for hyperspectral image processing, which can effectively reduce the data redundancy and computation time. In the case of unknown class labels, it is very difficult to select an effective band subset. In this paper, an unsupervised band selection algorithm is proposed which can preserve the original information of the hyperspectral image and select a low-redundancy band subset. First, a search criterion is designed to effectively search the best band subset with maximum information entropy. It is challenging to select a low-redundancy spectral band subset with maximizing the search criteria since it is a NP-hard problem. To overcome this problem, a double-graph model is proposed to capture the correlations between spectral bands with full use of the spatial information. Then, an improved Determinantal Point Process algorithm is presented as the search method to find the low-redundancy band subset from the double-graph model. Experimental results verify that our algorithm achieves better performance than other state-of-the-art methods.