Subspace clustering for hyperspectral images via dictionary learning with adaptive regularization

Sparse subspace clustering (SSC) has emerged as an effective approach for the automatic analysis of hyperspectral images (HSI). Traditional SSC-based approaches employ the input HSI data as a dictionary of atoms, in terms of which all the data samples are linearly represented. This leads to highly redundant dictionaries of huge size and the computational complexity of the resulting optimization problems becomes prohibitive for large-scale data. In this paper, we propose a scalable subspace clustering method, which integrates the learning of a concise dictionary and robust subspace representation in a unified model. This reduces significantly the size of the involved optimization problems. We introduce a new adaptive spatial regularization for the representation coefficients, which incorporates spatial information of HSI and improves the robustness of the model to noise. We derive an effective solver based on alternating minimization and alternating direction method of multipliers (ADMM) to solve the resulting optimization problem. Experimental results on four representative hyperspectral images show the effectiveness of the proposed method and excellent clustering performance relative to the state-of-the-art