ORIGINAL RESEARCH article
Front. Earth Sci.
Sec. Geohazards and Georisks
Volume 13 - 2025 | doi: 10.3389/feart.2025.1715960
This article is part of the Research TopicMonitoring, Early Warning and Mitigation of Natural and Engineered Slopes – Volume VView all 9 articles
Automatic Extraction Algorithm for Landslide Cracks Using Insar-UAV LiDAR Point Cloud Coupling
Provisionally accepted
- State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, China
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In response to the bottleneck problems of weak landslide crack morphology, hidden features, and limited extraction accuracy in complex terrain masking and dense vegetation coverage environments, as well as the shortcomings of existing methods in cross scale and multi-source heterogeneous data fusion, this study proposes an automatic landslide crack extraction algorithm based on InSAR and UAV LiDAR point cloud collaboration. This algorithm relies on SBAS InSAR technology to achieve large-scale, long-term surface deformation monitoring, and identifies landslide deformation active areas through deformation rate threshold division and spatial clustering. In terms of fusion mechanism, a combination of control point matching and ICP (Iterative Closest Point) algorithm is adopted to accurately register the deformation zone data obtained by InSAR monitoring with the point cloud data obtained by UAV LiDAR, achieving effective fusion of cross scale and multi-source heterogeneous data. On this basis, guide the UAV LiDAR to conduct targeted fine scanning and obtain high-resolution 3D point cloud data. Based on point cloud, a three-dimensional model of landslide crack development area is constructed, and multidimensional morphological features such as width, direction, slope, and curvature are extracted. Discriminant feature vectors are constructed, and a probabilistic neural network (PNN) model is introduced to achieve probability classification of crack pixels through Gaussian kernel density estimation and Bayesian decision mechanism. Finally, edge extraction is optimized by Canny operator to achieve automated and high-precision recognition of crack contours. Fifty independent test cases were selected for the experiment, covering various types of landslides such as shallow soil landslides and rock landslides. The results showed that the proposed method performed well in multi vegetation covered environments, with IoU stability above 0.94, significantly better than existing mainstream methods, and had good robustness and engineering applicability.
Keywords: InSAR, UAV lidar, Deformation rate, Landslide Cracks, Automatic Extraction, Probabilistic neural network
Received: 30 Sep 2025; Accepted: 23 Oct 2025.
Copyright: © 2025 Deng, Yang, Yu, Pu and He. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Biao Yang, yangbiao50031@163.com
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