Spatial-aware hyperspectral image classification via multifeature kernel dictionary learning

Sparse representation based on dictionary learning has yielded impressive effects on hyperspectral image (HSI) classification. But most of these methods utilize only the single spectral feature of HSI and advanced features are not considered, such that the discriminability of sparse representation coefficients is relatively weak. In this paper, we propose a novel multifeature spatial aware dictionary learning model by incorporating complementary across-feature and contextual information obtaining from HSI. The newly developed model, by designing a joint sparse regularization term for pixels represented by several complementary yet correlated features in a contextual group, makes the learning sparse coefficients have enough discriminability. Also, in order to further improve the discrimination ability of coding coefficients, utilizing kernel trick, we design the corresponding kernel extension of the newly proposed model. Based on the newly presented models, we give two corresponding discriminant dictionary learning algorithms. The experimental results on Indian Pines and University of Pavia images show that the effectiveness of the proposed algorithms, which also validate that our algorithms can obtain more discriminant coding coefficients.