Topological Structure and Semantic Information Transfer Network for Cross-Scene Hyperspectral Image Classification

Domain adaptation techniques have been widely applied to the problem of cross-scene hyperspectral image (HSI) classification. Most existing methods use convolutional neural networks (CNN) to extract statistical features from data and often neglect the potential topological structure information between different land cover classes. CNN-based approaches generally only model the local spatial relationships of the samples, which largely limits their ability to capture the non-local topological relationship that would better represent the underlying data structure of HSI. In order to make up for the above shortcomings, a Topological structure and Semantic information Transfer network (TSTnet) is developed. The method employs the graph structure to characterize topological relationships and the graph convolutional network (GCN) that is good at processing for crossscene HSI classification. In the proposed TSTnet, graph optimal transmission (GOT) is used to align topological relationships to assist distribution alignment between source domain and target domain based on maximum mean difference (MMD). Furthermore, subgraphs from source domain and target domain are dynamically constructed based on CNN features to take advantage of the discriminative capacity of CNN models that in turn improves robustness of classification. In addition, to better characterize the correlation between distribution alignment and topological relationship alignment, a consistency constraint is enforced to integrate the output of CNN and GCN. Experimental results on three cross-scene HSI datasets demonstrate that the proposed TSTnet performs significantly better than some state-of-the-art domain-adaptive approaches.