A method of urban road settlement monitoring combining Deep-ResUnet and PS-InSAR:a case study of Hefei city

doi:10.13474/j.cnki.11-2246.2023.0234

Abstract

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.

Key words: PS-InSAR, semantic segmentation, road extraction, road deformation, Sentinel-1A

CLC Number:

ZOU Xin, WANG Lei, LI Jingyu, TENG Chaoqun, HUANG Jinzhong, LI Zhong, LI Shibao. A method of urban road settlement monitoring combining Deep-ResUnet and PS-InSAR:a case study of Hefei city[J]. Bulletin of Surveying and Mapping, 2023, 0(8): 67-71.

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