Total Variation Regularized Collaborative Representation Clustering With a Locally Adaptive Dictionary for Hyperspectral Remote Sensing Imagery

In this paper, we propose total variation regularized collaborative representation clustering with a locally adaptive dictionary for hyperspectral remote sensing imagery. With regard to the high redundancy of the global dictionary and the interference of unrelated dictionary atoms in the representation process, the collaborative representation clustering model with a locally adaptive dictionary is introduced to more precisely represent each pixel only with highly correlated atoms. In addition, total variation regularization is integrated to better account for the rich spatial contextual information. The extensive experimental results clearly illustrate the superiority of the proposed algorithm.