发表机构
College of Information Science and Engineering, Hunan Normal University; Institute of Information Engineering, Chinese Academy of Sciences; School of Artificial Intelligence and Robotics, Hunan University（湖南师范大学信息科学与工程学院; 中国科学院信息工程研究所; 湖南大学人工智能与机器人学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对高光谱图像融合中几何约束建模难题，提出双域流形建模框架，通过拓扑感知Transformer和频率解耦的空间 - 光谱协同融合模块，有效整合光谱与空间信息，在多数据集实验中表现优于现有方法。
AI 中文摘要
在高光谱图像融合中，实现光谱丰富性和空间保真度的连贯整合一直是核心目标。然而，现有方法难以有效建模几何约束。在空间域，空间 - 光谱交互弱限制了几何感知特征学习并抑制高频结构信息，导致低频偏差和结构退化。在光谱域，由光谱相似性引起的局部流形结构未被充分利用。为此提出双域流形建模（DDMM）框架，引入结合全局注意力与邻域传播的拓扑感知Transformer（TPFormer），还设计了频率解耦的空间 - 光谱协同融合（FDSCF）模块。实验表明DDMM在空间结构保留和光谱重建方面优于现有方法。
英文摘要
Achieving a coherent integration of spectral richness and spatial fidelity remains a central objective in hyperspectral image fusion. However, existing hyperspectral image fusion methods struggle to effectively model geometric constraints. In the spatial domain, weak spatial-spectral interaction limits geometry-aware feature learning and suppresses high-frequency structural information, resulting in low-frequency bias and structural degradation. In the spectral domain, local manifold structures induced by spectral similarity are insufficiently exploited, limiting intrinsic pixel relationship modeling and fine-grained spectral reconstruction. To address these challenges, we propose a dual-domain manifold modeling (DDMM) framework. Specifically, we introduce a Topology-Aware Transformer (TPFormer) that combines global attention with neighborhood propagation, jointly modeling spatial topology and pixel-level feature manifold relationships to capture intrinsic spatial-spectral structures and improve topology-aware representation learning. Furthermore, a Frequency-Decoupled Spatial-Spectral Collaborative Fusion (FDSCF) module is devised, in which features are projected into the frequency domain via the discrete cosine transform and explicitly decoupled into low- and high-frequency components. Guided by a low-rank structural prior and spectral-driven spatial enhancement, FDSCF selectively enhances geometry-aware high-frequency features, strengthening spatia-spectral coupling and recovering sharper edges and finer textures. Extensive experiments on multiple benchmark datasets demonstrate that DDMM achieves superior overall performance over SoTA methods in terms of spatial structure preservation and spectral reconstruction.
