Clustering information-constrained 3D U-Net subspace clustering for hyperspectral image

Hyperspectral image (HSI) clustering is a challenging task due to the complex spatial-spectral structure and high-dimensional property in HSI data. In this letter, a novel clustering information-constrained 3D U-Net subspace clustering network is proposed for HSI clustering. Considering the spatial-spectral information, the proposed network takes the 3D pixel cubes around the pixels as the input. Based on the 3D pixel cubes, a 3D U-Net subspace clustering network is introduced to extract spatial-spectral features from 3D pixel cubes and learn self-representation subspace property among pixels. In order to learn features more suitable for clustering, a clustering information constraint is introduced to explore useful information gain in the existing clustering result. Experiments conducted on three public HSI datasets illustrate the superior performance of the proposed method.