Domain Adaptation Based on Graph and Statistical Features for Cross-Scene Hyperspectral Image Classification

Cross-scene hyperspectral image (HSI) classification has gradually received widespread attention, because most models perform unsatisfactory classification performance on training and testing samples from two different scenes. At present, the domain adaptation technique is used to solve this problem, most of which only design models from the level of data statistical features, while ignore the potential topological relationships between the land cover classes. In order to make up for the above shortcoming, a domain adaptation based on graph and statistical features is proposed in the papaer. This method uses convolutional neural network (CNN) extracting features with rich semantic information to dynamically construct graphs, and further introduces graph optimal transport (GOT) to align topological relations to assist distribution alignment based on maximum mean discrepancy (MMD). The experimental results on two cross-scene HSI datasets demonstrate the effectiveness of the proposed method.