Robust Multimodal Road Extraction via Dual-Layer Evidential Fusion Networks for Remote Sensing

Accurate road network extraction from remote sensing images (RSIs) is essential for applications such as urban planning, map updates, and autonomous navigation. However, challenges such as complex backgrounds, varying spatial resolutions, and occlusions hinder traditional single-modality approaches, which often fail to capture comprehensive contextual information. To address these limitations, we propose the DEFNet, a novel dual-layer evidential fusion network for robust multimodal road extraction. The DEFNet features two key modules: cross-attention feature interaction (CAFI) and dual-layer evidential fusion (DEF). The CAFI module facilitates adaptive multimodal interaction at both pixel and superpixel levels, enhancing feature fusion while mitigating noise. The DEF module, leveraging the Dirichlet framework and Dempster–Shafer theory (DST), performs uncertainty-aware fusion, improving prediction reliability and robustness. Extensive experiments on multiple benchmark datasets demonstrate that the DEFNet consistently outperforms state-of-the-art methods in both accuracy and robustness, making it highly effective for multimodal road extraction in remote sensing applications. The codes can be downloaded from https://github.com/BeechburgPieStar/DEFNet