MDCA-Net: a multi-directional alignment and dynamic context aggregation network for optical and SAR image fusion

Optical images and synthetic aperture radar (SAR) data exhibit complementary advantages, offering rich spectral and spatial information. The fusion of these modalities to enhance the quality of remote sensing images has garnered increasing attention in recent years. However, fusing optical and SAR images remains challenging due to differences in imaging mechanisms, speckle noise in SAR data, and the difficulty in jointly preserving details, structure, and spectral fidelity. To address these challenges, this paper proposes a multi-directional alignment and dynamic context aggregation network (MDCA-Net) for optical and SAR image fusion, designed to effectively exploit the complementary features of both modalities to generate information-rich fused images. Specifically, the cross-modal multi-directional alignment (CMDA) module is designed to mitigate discrepancies caused by differing imaging mechanisms. To suppress speckle noise and enhance structural details in SAR images, SAR dynamic context detail enhancement (SDCE) module is developed. Furthermore, a globally shift-aware context aggregation (GSCA) module is designed to jointly preserve detail, structural coherence, and spectral fidelity in the fused image. Compared with seven representative fusion methods, MDCA-Net demonstrates superior visual quality and achieves more outstanding quantitative and qualitative evaluation results. In addition, MDCA-Net significantly improves classification accuracy across multiple land cover types, including wetland, built-up area, grassland, forest, and cropland. On the Hunan dataset, MDCA-Net achieves gains of 4.48%, 2.63%, and 4.81% in mean intersection over union (mIoU), mean producer’s accuracy (mPA), and mean precision (mPrecision), respectively.