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- W1963764791 abstract "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." @default.
- W1963764791 created "2016-06-24" @default.
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- W1963764791 date "2014-10-01" @default.
- W1963764791 modified "2023-10-03" @default.
- W1963764791 title "Task-driven dictionary learning for hyperspectral image classification with structured sparsity priors" @default.
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- W1963764791 doi "https://doi.org/10.1109/icip.2014.7026065" @default.
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