Task-driven dictionary learning with different Laplacian priors for hyperspectral image classification
Task-driven dictionary learning (TDDL) has shown great success in many classification applications. However, the performance of TDDL is limited by the challenging properties of hyperspectral images (HSI). Fortunately, previous research has made significant progress in HSI classification by enforcing various structured sparsity constraints (priors) on the TDDL-based model. In this paper, we extend some previous work by relaxing the structured sparsity priors and make the model become more flexible and powerful. Specifically, we add class label Laplacian sparsity constraints in two different places, either on the sparse code or on the classifier outputs. Experimental results on widely used datasets shown improvement in performance compared to current state-of-the-art approaches.