DEDU-Net: Dual-Encoder-Decoder-U-Net for road extraction from high-resolution remote sensing images

Abstract

ABSTRACT The development of the modern urban economy is closely tied to road construction, with roads being one of the most crucial components in urban development. The use of high-resolution remote sensing images to monitor the road conditions in cities and surrounding towns has become a highly emphasized research focus in recent years. However, due to the complexity of the environment, this task still faces significant challenges. For instance, urban road structures are intricate, which involve multiple lanes, intersections, building obstructions, etc. Rural roads may consist of narrow paths or irregular dirt roads. In addressing these issues, this paper proposes a road extraction algorithm based on a Dual-Encoder-Decoder U- Net (DEDU-Net). The algorithm leverages a dual encoder-decoder network to extract multi-scale information from the image. It then employs a dual decoder network to restore the feature map to the original image, achieving precise road extraction. Additionally, a new module, the Global Fusion Module (GFM), is introduced. This module achieves global context information fusion by weighting features. In the experimental section, two publicly available datasets were used for testing: the CHN6-CUG dataset and the Gansu Mountain Road dataset. For example, for the Gansu Mountain Road and CHN6-CUG mixed dataset, the model achieved an Overall Accuracy (OA) of 94.947% and a mean Intersection over Union (mIoU) of 70.971%. The results indicate that compared to traditional methods, this proposed method exhibits higher accuracy and robustness. It can adapt to both urban and rural roads, delivering outstanding performance even in complex scenarios.