Hyperspectral Image Clustering Based on Unsupervised Broad Learning

Abstract

Due to the difficulty of labeling a large number of training samples of a hyperspectral image (HSI), unsupervised clustering methods have drawn great attention. The recently proposed broad learning (BL) can implement both linear and nonlinear mappings. However, the original BL is a supervised model. In this letter, a novel method named unsupervised BL (UBL) is introduced for HSI clustering. First, a graph-regularized sparse autoencoder is performed on the input and mapped feature of UBL in order to maintain the intrinsic manifold structure of origin HSI. Then, the objective function of UBL composed of an $l_{2}$ -norm of output-layer weights and a graph regularization term is designed, which can be easily solved by choosing eigenvectors corresponding to the smallest eigenvalues. Finally, the HSI clustering results can be obtained by applying spectral clustering on the output of UBL. Experiments on three popular real HSI data sets demonstrate that, compared with several competitive methods, UBL can achieve better clustering performance.