LAUSR

Abstract In order to effectively exploit the intra-class and inter-class structure information, we propose a new class-wise dictionary learning method for hyperspectral image classification. First, we construct two special manifold regularizers to encourage intra-class basis sharing and inter-class basis competition, and the regularizers are incorporated into the objective function to learn a discriminative class-wise dictionary. Then the sparse representations can be obtained via the learned class-wise dictionary under the collaborative representation framework. Finally, we put the sparse representations of the data into the support vector machine (SVM) for training and then apply the SVM classifiers to predict labels for the test set. The experimental results obtained on two hyperspectral datasets demonstrate that the proposed method can obtain higher classification accuracy with much lower computational cost compared with other traditional classifiers.