Hyper-Graph Regularized Kernel Subspace Clustering for Band Selection of Hyperspectral Image

Abstract

Band selection is an effective way to deal with the problem of the Hughes phenomenon and high computation complexity in hyperspectral image (HSI) processing. Based on the hypothesis that all the pixels are sampled from the union of subspaces, many robust band selection algorithms based on subspace clustering were introduced in recent works, achieving significant performances. However, these methods focus on linear subspaces, which are not suitable for the typical nonlinear structure of HSIs. In this paper, to deal with these obstacles, a new hyper-graph regularized kernel subspace clustering (HRKSC) is presented for band selection of hyperspectral image. The proposed approach extends subspace clustering to nonlinear manifold by utilizing the kernel trick, which can better fit the nonlinear structure of HSIs. The hyper-graph regularized is introduced to consider the manifold structure reflecting geometric information and accurately describe the multivariate relationship between data points, which makes the modeling of HSIs more accurate. The results of the proposed algorithm are compared with existing band selection methods on three well-known hyperspectral data sets, showing that the HRKSC algorithm can accurately select an informative band subset and outperforming the current state-of-the-art band selection methods.