Class Probability Propagation of Supervised Information Based on Sparse Subspace Clustering for Hyperspectral Images

Hyperspectral image (HSI) clustering has drawn increasing attention due to its challenging work with respect to the curse of dimensionality. In this paper, we propose a novel class probability propagation of supervised information based on sparse subspace clustering (CPPSSC) algorithm for HSI clustering. Firstly, we estimate the class probability of unlabeled samples by way of partial known supervised information, which can be addressed by sparse representation-based classification (SRC). Then, we incorporate the class probability into the traditional sparse subspace clustering (SSC) model to obtain a more accurate sparse representation coefficient matrix accompanied by obvious block diagonalization, which will be used to build the similarity matrix. Finally, the cluster results can be obtained by applying the spectral clustering on similarity matrix. Extensive experiments on a variety of challenging data sets illustrate that our proposed method is effective.