LGD-Net: Leader-Guided Cross-Modal Dynamics for Hyperspectral and Panchromatic Image Fusion

Hyperspectral-panchromatic fusion reconstructs high-resolutionhyperspectral (HRHS) images by combining low-resolution hyperspectral(LRHS) observations with high-resolution panchromatic (PAN) imagery.Although recent deep architectures achieve strong spectral–spatial re-construction performance, most models rely on stacked layers in whichglobal cross-modal interactions and local spatial refinement are updatedjointly at every stage. Consequently, the influence of PAN information isimplicitly coupled to network depth, limiting independent control overcross-modal interaction and reducing flexibility in balancing spectralpreservation and spatial enhancement. We propose the Leader-GuidedDynamics Network (LGD-Net), a framework that explicitly decouplesglobal cross-modal interaction from local multi-scale reconstruction. Aglobal leader representation first aggregates modality-specific features tosummarize global context. Instead of propagating interactions throughrepeated stacking, only the leader state evolves through a controlled,continuous-depth dynamic process, while local feature nodes focus onspatial reconstruction. The refined leader is subsequently injected backinto the multi-scale feature hierarchy via structured residual modula-tion, providing global guidance without increasing architectural depth.By separating global state evolution from local reconstruction, LGD-Net enables explicit control over cross-modal interactions while main-taining computational efficiency. Experiments on multiple hyperspectralbenchmarks demonstrate improved spectral–spatial reconstruction qual-ity compared to conventional stacked fusion architectures.