Cross-Scene Diffusion-Enhanced Uncertainty Attention Network for Hyperspectral Image Classification

Cross-scene hyperspectral image (HSI) classification is widely used for its exceptional ability to address the small-sample-size problem. However, important challenges remain, including domain shift and noise between scenes, the need to align features while accounting for sample-level reliability, and the difficulty of fusing domain-invariant and scene-specific features without degrading discriminability. To enhance generalization and reliability with small sample sizes, we propose a cross-scene diffusion-enhanced uncertainty attention network (CDUAN). CD-UAN consists of three main components. 1) A domain adaptation diffusion module (DADM) utilizes target-guided diffusion to synthesize augmented source samples, establishing distribution-level alignment as the first step. 2) A cross-scene uncertainty-aware mutual attention module (CUMAM) is then proposed to ensure feature-level reliability control during alignment across scenes by integrating a sample-level uncertainty mechanism. This mechanism adjusts attention weights to highlight domain-invariant features and, during fusion, retain as much discriminative information as possible. 3) A cross-scene fusion attention module (CSFAM) realizes fusion-level control of invariance and discriminability across scenes by combining multi-scale contrastive pairing with attention-based fusion. CSFAM maximizes relevant cross-scene feature similarity, suppresses noise, and ensures discriminative representation for accurate classification. Extensive experiments on three cross-scene datasets validate the effectiveness of the proposed CDUAN.