Focal Transfer Graph Network and Its Application in Cross-Scene Hyperspectral Image Classification

Affected by the sensor, shooting environment, and other aspects, hyperspectral images (HSIs) in the source and target domains exhibit phenomenon of difficult feature extraction and domain shift. The above phenomena pose challenges to the cross-scene HSI classification task. Therefore, a focal transfer graph network (FTGN) for cross-scene HSI classification is proposed. First, FTGN leverages graph sample and aggregate (GraphSAGE) to capture spatial–spectral features by aggregating partial adjacency nodes, ensuring the acquisition of contextual information. The neighbor weighting strategy based on spatial–spectral information is proposed to solve the information interference caused by excessive node aggregation. Second, a pseudolabel trimming strategy based on class metrics is proposed to reduce the adverse effects of pseudolabel noise in the transfer process. Then, a specification subdomain adaptation (SSA) module is proposed, which helps to achieve effective distribution alignment by reducing the feature distance of intraclass samples and widening the feature distance of interclass samples during the subdomain adaptation process. Finally, the focal loss is utilized to help FTGN focus on hard-to-classify samples. The experimental results on eight data pairs show that the proposed method outperforms several state-of-the-art methods.