Robust Estimation of Landslide Displacement from Multitemporal UAV Photogrammetry-Derived Point Clouds

Abstract

Existing algorithms based on remote sensing for landslide displacement estimation, such as C2C, C2M, DOD, and M3C2, are sensitive to errors generated in data processing, and further improving their accuracy is difficult. To address this issue, given that redundant observations may occur in landslide monitoring, we proposed a robust estimation method of landslide displacement from multitemporal unmanned aerial vehicle (UAV) photogrammetry-derived point clouds. The proposed method first establishes the relevant graph to manage the trajectory of error propagation for landslide displacement estimation for all possible paths. Two modules, namely intra- and inter-estimates, are explored to reduce the impact of outliers and high surface roughness in point clouds derived by UAV photogrammetry. Individually, the intraestimate operation is used to calculate landslide displacement between two temporal point clouds by robust estimation considering outlier constraint, and the interestimate operation is used to obtain the optimal calculation of landslide displacement by minimizing a given objective function using IGG robust estimation proposed by the Institute of Geodesy and Geophysics at the Chinese Academy of Sciences. Experimental results show that the proposed method is significantly superior to conventional methods, such as C2C, C2M, and M3C2, with an accuracy improvement of at least 8%.