Structure-Prior-Constrained Low-Rank and Sparse Representation With Discriminative Incremental Dictionary for Hyperspectral Image Classification

Low-rank and sparse representation (LRSR) model has gained popularity in hyperspectral image (HSI) classification. However, most existing LRSR models are limited by the highly nonlinear correlation of hyperspectral data, which leads to poor subspace segmentation performance. Furthermore, current LRSR methods usually directly used labeled samples to build the dictionary, whereas low discriminative labeled samples may degrade the representation ability of the dictionary. To solve the above issues, we propose a novel structure-prior-constrained LRSR with discriminative incremental dictionary (SPCLSR-DID) method for HSI classification. First, global and local data structures are maintained by low-rank and sparsity constraints, while a structural prior constraint is introduced to explore the intrinsic spectral–spatial structural information of HSI, improving the subspace segmentation ability of the model. Second, a discriminative incremental dictionary (DID) method is presented to find reliable and discriminative augmented atoms to improve the completeness and representation power of the dictionary. In DID, the incremental dictionary size is controllable to suit different tasks. Finally, the class label of each target sample is determined by jointly considering contextual information within a certain local range, which ensures the accuracy and smoothness of the classification map. Experimental results based on four popular hyperspectral datasets demonstrate that the proposed SPCLSR-DID method significantly outperforms other related comparison methods in terms of classification accuracy and generalization performance.