Sparse and Low-Rank Representation With Key Connectivity for Hyperspectral Image Classification

Combined techniques of sparse representation (SR) and low-rank representation (LRR) are commonly used for hyperspectral image (HSI) classification. Although they have the ability to capture the interclass representations of data for HSI classification, they ignore the adaptive key connectivity of the learned intraclass data representations in particular with the high-dimensional complex HSI data. It is well-known that the key connectivity of graph-based algorithms is crucial for subspace learning because of the guarantees of its good neighbors. For this purpose, a novel sparse and low-rank representation with key connectivity (SLRC) method is proposed for HSI classification. To be specific, the adaptive probability graph structure is developed to integrate the SR and LRR regularizations to formulate the SLRC model, which flexibly perform discriminative latent subspace construction and preserve the key connectivity of intraclass representations. Then, extensive experiments are executed based on three popular HSI datasets, which demonstrates that the SLRC method outperforms the other popular methods.