Research on surface deformation recognition technology based on UAV inspection and InSAR data

Abstract

In alpine and canyon areas, the problems of reservoir bank deformation and landslides are prominent. How to quickly and efficiently monitor deformation, timely identify geological disasters, and carry out surveys and treatments has become a crucial issue to be resolved urgently. Traditional survey methods are restricted by factors such as steep terrain and inconvenient transportation, resulting in low efficiency. In recent years, remote sensing technology has developed rapidly in the field of geological disaster monitoring, thanks to its high-precision deformation monitoring capabilities. This study is based on the Baihetan Hydropower Station during the water storage stage. The InSAR deformation monitoring technology is used to conduct large-scale disaster risk screening of the reservoir bank slopes. Firstly, the study analyzes the characteristics of UAV laser point clouds and images to construct a point cloud sequence. Subsequently, an improved iterative closest point (ICP) algorithm that integrates the scale-invariant feature transform (SIFT) and cylindrical neighborhood search is applied to improve the accuracy of slope deformation extraction. Finally, with the help of recognition algorithms and practical engineering experience, the surface deformation is analyzed based on the measured terrain data. The research shows that the surface deformation recognition technology based on UAV inspection and InSAR data has significant advantages in the monitoring of geological disasters in reservoirs in alpine and canyon areas. It can detect potential hazards in a timely manner and provide key decision-making basis for engineering projects.

Keywords

surface deformation UAV inspection InSAR technology geological hazards point cloud data

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