Bridging the Style Gap: Style-Guided Distillation Domain Adaptation for Hyperspectral Image Classification

Land cover in different hyperspectral images (HSIs) commonly exhibits style differences and similarities in the same category and distinct categories. However, most of the existing cross-scene HSI classification methods overlook this issue and only conduct the feature-level alignment with unsupervised domain adaptation (UDA). To address this limitation, we propose the style-guided distillation domain adaptation (SGDDA) for HSI classification. First, a Fourier transform-based style transfer (FTST) module is proposed to generate an enhanced source HSI. It transfers stylistic features from the target domain (TD) to the source domain (SD) by substituting low-frequency components, thereby preserving semantic invariance while bridging their style gap. Second, the dual-path knowledge distillation (DPKD) module is designed to reduce ambiguity in category assignment for the SD. This is achieved through cross-domain consistency learning between original SD samples and their enhanced style-transferred counterparts, ensuring robust feature alignment across domains. Third, unlike existing methods that primarily utilize a classification threshold to select pseudo-label for target samples, we propose the confidence-aware dual pseudo-label consensus (CADPLC) strategy. This strategy dynamically selects the reliable pseudo-labels by leveraging both class prototype matching and teacher–student prediction consensus, eliminating reliance on fixed thresholds and significantly improving adaptation to the TD. Experiments on three benchmark datasets demonstrate the superiority of the proposed SGDDA in comparison with several state-of-the-art methods. The code is available at https://github.com/Wei-spvl/SGDDA