Task-driven dictionary learning for hyperspectral image classification with structured sparsity priors

In hyperspectral pixel classification, previous research have shown that the sparse representation classifier can achieve a better performance when exploiting the neighboring test pixels through enforcing different structured sparsity priors. In this paper, we propose a supervised sparse-representation-based dictionary learning method with joint or Laplacian s-parsity priors. The proposed method has numerous advantages over the existing dictionary learning techniques. It uses a structured sparsity and provides a more robust and stable sparse coefficients. Besides, it is capable of reducing the classification error by jointly optimizing the dictionary and the classifier's parameters during the dictionary training stage.