DAS-Net: A Dual-Branch Structure-Aware Network for SAR–Optical Image Registration in Agricultural and Natural Scenes

Accurate registration of synthetic aperture radar (SAR) and optical images is essential for multimodal data fusion, yet the nonlinear radiometric discrepancies and speckle noise inherent in SAR imagery make this task highly challenging. We propose DAS-Net, a lightweight Dual-branch, Attention-guided, Structure-aware Network designed to address these difficulties. The network introduces three key contributions: a modality-adaptive dual-branch feature extractor to capture robust structural cues, a multi-scale context aggregation module with attention to enhance geometry-consistent representations, and a frequency-domain-driven coarse-to-fine matching strategy that achieves sub-pixel alignment. In addition, a structure-aware matching loss jointly enforces global semantic alignment and local spatial consistency. Extensive experiments on three SAR–optical datasets (two public and one self-constructed) show that DAS-Net consistently outperforms state-of-the-art handcrafted and learning-based methods in terms of matching precision, number of correct correspondences, and registration accuracy. The network also demonstrates strong adaptability in large-scale farmland scenes characterized by weak textures and repetitive patterns, where conventional approaches often fail. These results confirm the effectiveness and efficiency of DAS-Net for SAR–optical image registration and its potential for multimodal remote sensing applications.