MRLF: Multi-Resolution Layered Fusion Network for Optical and SAR Images

To enhance the comprehensive representation capability and fusion accuracy of remote sensing information, this paper proposes a multi-resolution hierarchical fusion network (MRLF) tailored to the heterogeneous characteristics of optical and synthetic aperture radar (SAR) images. By constructing a hierarchical feature decoupling mechanism, the method decomposes input images into low-resolution global structural features and high-resolution local detail features. A residual compression module is employed to preserve multi-scale information, laying a complementary feature foundation for subsequent fusion. To address cross-modal radiometric discrepancies, a pre-trained complementary feature extraction model (CFEM) is introduced. The brightness distribution differences between SAR and fusion results are quantified using the Gram matrix, and mean-variance alignment constraints are applied to eliminate radiometric discontinuities. In the feature fusion stage, a dual-attention collaborative mechanism is designed, integrating channel attention to dynamically adjust modal weights and spatial attention to focus on complementary regions. Additionally, a learnable radiometric enhancement factor is incorporated to enable efficient collaborative representation of SAR textures and optical semantics. To maintain spatial consistency, hierarchical deconvolution and skip connections are further used to reconstruct low-resolution features, gradually restoring them to the original resolution. Experimental results demonstrate that MRLF significantly outperforms mainstream methods such as DenseFuse and SwinFusion on the Dongying and Xi’an datasets. The fused images achieve an information entropy (EN) of 6.72 and a structural similarity of 1.25, while maintaining stable complementary feature retention under large-scale scenarios. By enhancing multi-scale complementary features and optimizing radiometric consistency, this method provides a highly robust multi-modal representation scheme for all-weather remote sensing monitoring and disaster emergency response.