Graph Regularized Residual Subspace Clustering Network for hyperspectral image clustering

Abstract

Hyperspectral image (HSI) clustering is a challenging task due to the high complexity of HSI data. Subspace clustering methods have achieved promising clustering performance in HSI clustering. However, the existing subspace clustering methods calculate linear affinity rather than nonlinear relationships between data points and often ignore high-level feature extraction, resulting in low clustering accuracy. To address these issues, this paper proposes a Graph Regularized Residual Subspace Clustering Network (GR-RSCNet) that jointly learns deep spectral-spatial representation and robust nonlinear affinity via a deep neural network. Technically, we recast a graph regularized subspace clustering model as a special self-expressive (SE) layer that is integrated into a deep residual convolutional autoencoder. Benefitting from the residual structure, GR-RSCNet can be easily trained from scratch by optimizing a joint loss function consisting of reconstruction error, global manifold regularization term, and self-representation loss. We extensively evaluate the proposed GR-RSCNet for four popular HSI datasets, demonstrating that the GR-RSCNet can achieve state-of-the-art clustering results that significantly outperform many prior arts.