Agreement–Disagreement Guided Knowledge Transfer for Cross-Scene Hyperspectral Imaging

Knowledge transfer plays a crucial role in cross-scene hyperspectral imaging (HSI). However, existing studies often overlook the challenges of gradient conflicts and dominant gradients that arise during the optimization of shared parameters. Moreover, many current approaches fail to simultaneously capture both agreement and disagreement information, relying only on a limited shared subset of target features and consequently missing the rich, diverse patterns present in the target scene. To address these issues, we propose an Agreement–Disagreement Guided Knowledge Transfer (ADGKT) framework that jointly models optimization consistency and representation diversity for heterogeneous cross-scene HSI classification. The proposed framework consists of two complementary mechanisms. The agreement mechanism stabilizes joint optimization by mitigating gradient conflicts and balancing the contributions of source and target domains during shared parameter learning. The disagreement mechanism explicitly preserves complementary target-specific representations through a dedicated disagreement branch and integrates transferable and target-critical information into a unified predictor. Unlike conventional transfer learning approaches that rely solely on feature alignment, the proposed framework simultaneously encourages transferable knowledge sharing and target-specific representation learning, thereby improving robustness under heterogeneous scene discrepancies. Extensive experiments demonstrate the effectiveness and superiority of the proposed method in achieving robust and balanced knowledge transfer across heterogeneous HSI scenes.