Automatic extraction algorithm for landslide cracks using Insar-UAV LiDAR point cloud coupling

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.