Adaptive Graph Modeling With Self-Training for Heterogeneous Cross-Scene Hyperspectral Image Classification

The small-sample-size problem of hyperspectral image (HSI) classification has recently gained considerable attention. Cross-scene HSI classification has emerged as an effective solution to this problem. In real-world applications, different HSI scenes are often captured by diverse sensors, resulting in variations between scenes. Graph modeling, as a method to represent relationships, leverages semantic information to establish connections between scenes, thereby facilitating transfer learning by aligning their features. However, in scenarios with only a few labeled target samples, the resulting graph is typically sparse and can only capture weak cross-scene relationships. Studies have shown that a dense and fault-tolerant graph is beneficial for transfer learning in small-sample-size cases. Consequently, we propose a novel heterogeneous transfer learning approach called adaptive graph modeling with self-training (AGM-ST). Unlike conventional graph modeling methods that employ predefined graph weights, adaptive graph modeling (AGM) employs a learnable network to generate graph weights based on the similarities of spectral-spatial features. Additionally, an adaptive cutoff threshold is trained to eliminate weak relationships between samples that may be potentially incorrect. Subsequently, a cross-scene graph loss is designed based on the generated graph to align the feature spaces of the source and target scenes. Furthermore, the unlabeled samples from the target scene are gradually updated with pseudo labels using the self-training (ST) technique, which enhances semantic information and improves graph modeling. Experimental evaluations conducted on three cross-scene HSI datasets have demonstrated the effectiveness of the proposed AGM-ST approach.