【摘要】 针对城市道路网变形监测存在的高分辨率影像获取难、道路人工提取效率低、传统变形监测工作量大等难题，本文提出了一种基于融合Deep-ResUnet和PS-InSAR的城市道路网形变监测方法。主要思路为：首先对目标区的哨兵-1A(Sentinel-1A)影像数据进行伪彩色变换建立道路数据集；然后训练深度残差网络(Deep-ResUnet)模型并对道路网栅格进行提取；最后利用永久散射体干涉测量(PS-InSAR)技术获取PS点形变信息与道路网栅格融合。研究结果表明，Sentinel-1A影像经过伪彩色处理后，能提高城市道路网提取的完整性，交并比提高6%～9%,道路提取精度平均提高10%左右，得到的城市道路网形变信息专题图能为城市道路变形监测和健康状况评估提供科学依据。更多还原

【Abstract】 In view of the problems of deformation monitoring of urban road network, such as difficulty in obtaining high-resolution images, low efficiency of manual road extraction, and heavy workload of traditional deformation monitoring, this paper proposes a deformation monitoring method of urban road network based on fusion of Deep-ResUnet and PS-InSAR. The main idea is to first perform pseudo color transformation on Sentinel-1A image data in the target area to establish a road dataset, then train a Deep-ResUnet model and extract the road network grid. Finally, the permanent scatterer interferometry(PS-InSAR) technique is used to obtain PS point deformation information and fuse it with the road network grid. The research results show that after the Sentinel-1A image is processed with pseudo color, the integrity of urban road network extraction can be improved, the intersection and merge ratio can be improved by 6%～9%, and the accuracy of road extraction can be improved by about 10% on average. The thematic map of urban road network deformation information obtained can provide scientific basis for urban road deformation monitoring and health assessment.更多还原