High-quality remote sensing (RS) image acquisition is fundamentally constrained by physical limitations. While Multi-Frame Super-Resolution (MFSR) and Pansharpening address this by exploiting complementary information, they are typically studied in isolation: MFSR lacks high-resolution (HR) structural priors for fine-grained texture recovery, whereas Pansharpening relies on upsampled low-resolution (LR) inputs and is sensitive to noise and misalignment. In this paper, we propose SatFusion, a novel and unified framework that seamlessly bridges multi-frame and multi-source RS image fusion. SatFusion extracts HR semantic features by aggregating complementary information from multiple LR multispectral frames via a Multi-Frame Image Fusion (MFIF) module, and integrates fine-grained structural details from an HR panchromatic image through a Multi-Source Image Fusion (MSIF) module with implicit pixel-level alignment. To further alleviate the lack of structural priors during multi-frame fusion, we introduce an advanced variant, SatFusion*, which integrates a panchromatic-guided mechanism into the MFIF stage. Through structure-aware feature embedding and transformer-based adaptive aggregation, SatFusion* enables spatially adaptive feature selection, strengthening the coupling between multi-frame and multi-source representations. Extensive experiments on four benchmark datasets validate our core insight: synergistically coupling multi-frame and multi-source priors effectively resolves the fragility of existing paradigms, delivering superior reconstruction fidelity, robustness, and generalizability.

翻译：高质量的遥感（RS）图像获取从根本上受限于物理条件的制约。虽然多帧超分辨率（MFSR）和全色锐化通过利用互补信息来解决这一问题，但它们通常被孤立研究：MFSR缺乏用于细粒度纹理恢复的高分辨率（HR）结构先验，而全色锐化依赖于上采样的低分辨率（LR）输入，并且对噪声和错位敏感。本文提出SatFusion，一个新颖且统一的框架，该框架无缝衔接了多帧与多源遥感图像融合。SatFusion通过多帧图像融合（MFIF）模块聚合来自多个LR多光谱帧的互补信息来提取HR语义特征，并通过具有隐式像素级对齐的多源图像融合（MSIF）模块整合来自HR全色图像的细粒度结构细节。为进一步缓解多帧融合过程中结构先验的缺失，我们引入了一个高级变体SatFusion*，它将全色引导机制集成到MFIF阶段中。通过结构感知特征嵌入和基于Transformer的自适应聚合，SatFusion*实现了空间自适应的特征选择，增强了多帧表示与多源表示之间的耦合性。在四个基准数据集上的大量实验验证了我们的核心观点：将多帧先验与多源先验协同耦合，能有效解决现有范式的脆弱性，提供卓越的重建保真度、鲁棒性和泛化能力。