What Should Be Shared Across Scenes? Self-Supervised Scene-Style-Conditioned Correspondence Learning for Hyperspectral Image Domain Adaptation

Cross-scene hyperspectral image (HSI) domain adaptation aims to transfer knowledge from a labeled source scene to an unlabeled target scene under substantial spectral and spatial differences. Existing methods mainly rely on feature alignment, pseudo-labeling, or cross-scene similarity to establish transferable relations between the two scenes. However, such relations are typically inferred directly from observed features or predictions, without separating scene-dependent spectral variation from class-related structure. To address this issue, we propose scene-style-conditioned correspondence learning (SCCL), a cross-scene adaptation framework that shares only relations that remain meaningful under scene-style variation. We first model scene-dependent feature statistics with a bounded stochastic scene-style posterior and construct scene-style transport under a spectral-admissibility constraint. We then examine whether source–target semantic relations persist across the resulting admissible views, yielding a correspondence-survival probability. The surviving relations are organized by pairwise correspondence admissibility and a globally normalized correspondence plan, which directly induces soft target semantics for final learning. Experiments on Houston, Pavia, and Shanghai–Hangzhou cover six directed cross-scene tasks, demonstrating the effectiveness and superiority of the proposed SCCL method. The code is available at https://github.com/Sky-byte-box/SCCL_code.