Yang Nanjie, Di Weimeng, Wang Qingyu, Liu Wansi, Feng Teng, Tian Xiaomin
School of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China.
Hebei Collaborative Innovation Center for Aerospace Remote Sensing Information Processing and Application, Langfang 065000, China.
Sensors (Basel). 2024 Oct 16;24(20):6672. doi: 10.3390/s24206672.
High-resolution remote sensing imagery, reaching meter or sub-meter levels, provides essential data for extracting and identifying road information. However, rural roads are often narrow, elongated, and have blurred boundaries, with textures that resemble surrounding environments such as construction sites, vegetation, and farmland. These features often lead to incomplete extraction and low extraction accuracy of rural roads. To address these challenges, this study introduces the RC-MSFNet model, based on the U-Net architecture, to enhance rural road extraction performance. The RC-MSFNet model mitigates the vanishing gradient problem in deep networks by incorporating residual neural networks in the downsampling stage. In the upsampling stage, a connectivity attention mechanism is added after dual convolution layers to improve the model's ability to capture road completeness and connectivity. Additionally, the bottleneck section replaces the traditional dual convolution layers with a multi-scale fusion atrous convolution module to capture features at various scales. The study focuses on rural roads in the Xiong'an New Area, China, using high-resolution imagery from China's Gaofen-2 satellite to construct the XARoads rural road dataset. Roads were extracted from the XARoads dataset and DeepGlobe public dataset using the RC-MSFNet model and compared with some models such as U-Net, FCN, SegNet, DeeplabV3+, R-Net, and RC-Net. Experimental results showed that: (1) The proposed method achieved precision (P), intersection over union (IOU), and completeness (COM) scores of 0.8350, 0.6523, and 0.7489, respectively, for rural road extraction in Xiong'an New Area, representing precision improvements of 3.8%, 6.78%, 7.85%, 2.14%, 0.58%, and 2.53% over U-Net, FCN, SegNet, DeeplabV3+, R-Net, and RC-Net. (2) The method excelled at extracting narrow roads and muddy roads with unclear boundaries, with fewer instances of omission or false extraction, demonstrating advantages in complex rural terrain and areas with indistinct road boundaries. Accurate rural road extraction can provide valuable reference data for urban development and planning in the Xiong'an New Area.
高分辨率遥感影像可达米级或亚米级，为提取和识别道路信息提供了重要数据。然而，农村道路通常狭窄、狭长且边界模糊，其纹理与建筑工地、植被和农田等周边环境相似。这些特征常常导致农村道路提取不完整且提取精度较低。为应对这些挑战，本研究引入了基于U-Net架构的RC-MSFNet模型，以提升农村道路提取性能。RC-MSFNet模型通过在降采样阶段引入残差神经网络来缓解深度网络中的梯度消失问题。在升采样阶段，在双卷积层之后添加连通性注意力机制，以提高模型捕捉道路完整性和连通性的能力。此外，瓶颈部分用多尺度融合空洞卷积模块取代传统的双卷积层，以捕捉不同尺度的特征。本研究聚焦于中国雄安新区的农村道路，使用中国高分二号卫星的高分辨率影像构建了XARoads农村道路数据集。利用RC-MSFNet模型从XARoads数据集和DeepGlobe公共数据集中提取道路，并与U-Net、FCN、SegNet、DeeplabV3+、R-Net和RC-Net等一些模型进行比较。实验结果表明：（1）所提方法在雄安新区农村道路提取中，精度（P）、交并比（IOU）和完整性（COM）得分分别达到0.8350、0.6523和0.7489，相较于U-Net、FCN、SegNet、DeeplabV3+、R-Net和RC-Net，精度提升了3.8%、6.78%、7.85%、2.14%、0.58%和2.53%。（2）该方法在提取狭窄道路和边界不清晰的泥泞道路方面表现出色，遗漏或误提取的情况较少，在复杂农村地形和道路边界不明显的区域展现出优势。准确的农村道路提取可为雄安新区的城市发展和规划提供有价值的参考数据。
