无人机贴近摄影技术在高陡边坡的三维重建与结构面识别中的应用  

Three-dimensional reconstruction and structural surface identification of high steep slopes based on UAV close-range photogrammetry

作　　者：王林峰[1,2] 蒋辉 唐宁 黄晓明[3] 谭国金[4] WANG Linfeng;JIANG Hui;TANG Ning;HUANG Xiaoming;TAN Guojin(Key Laboratories of Mountainous Area Highway Transportation and Transportation Geological Disaster Reduction in University of Chongqing,Chongqing Jiaotong University,Chongqing 400074,China;School of Hehai,Chongqing Jiaotong University,Chongqing 400074,China;School of Transportation,Southeast University,Nanjing,Jiangsu 211189,China;School of Transportation,Jilin University,Changchun,Jilin 130022,China)

机构地区：[1]重庆交通大学山区公路水运交通地质减灾重庆市高校市级重点实验室,重庆400074 [2]重庆交通大学河海学院,重庆400074 [3]东南大学交通学院,江苏南京211189 [4]吉林大学交通学院,吉林长春130022

出　　处：《中国地质灾害与防治学报》2025年第1期92-100,共9页The Chinese Journal of Geological Hazard and Control

基　　金：国家自然科学基金联合基金项目(U22A20600);国家重点研发计划课题项目(2021YFB2600604,2021YFB2600600);重庆交通大学研究生科研创新项目(2022B0005)。

摘　　要：地质灾害调查可及时发现隐患、发出预警,避免生命财产损失。为解决高陡边坡调查风险高、效率低等问题,提出了基于无人机贴近摄影的高陡边坡三维重建与结构面识别方法。以重庆南川甑子岩为例,首先通过无人机贴近摄影和航线补充摄影获取高清航拍图,利用SFM-MVS算法构建精细三维模型和三维点云;然后提出自适应KNN算法,提高点云共面性检测通过率,通过最小二乘法拟合最佳平面方程,利用遗传退火模糊C算法实现点云聚类;最后根据点云协方差矩阵特征值和特征向量反算点云平面参数和法向量,并完成结构面识别和结构面产状参数提取。结果表明点云共面性检测通过率达99.6%,识别产状最大差值仅为4.82°。研究成果可为高陡边坡地质信息快速获取、稳定性评价及防灾减灾提供思路。Geological disaster investigations enable timely detection of hazards,issuance of early warnings,and prevention of loss of life and property.To address the challenges of high risk and low efficiency of high steep slopes investigation,this study proposes a method of three-dimensional reconstruction and structural plane identification of high steep slope based on UAV close-range photogrammetry.Using Zengziyan in Nanchuan,Chongqing as a case study,the process begins with acquiring high-definition aerial photographs through UAV close-range and supplemental route photogrammetry.The SFM-MVS algorithm is utilized to construct detailed 3D models and point clouds.An adaptive KNN algorithm is introduced to enhance the coplanarity detection passing rate in point clouds,while optimal planar equations are fitted using the least squares method.Point cloud clustering is achieved using a genetic annealing fuzzy C algorithm.Finally,according to the point cloud covariance matrix eigenvalues and eigenvectors,the point cloud plane parameters and normal vectors are inverted,and the structural surface identification and structural surface yield parameters extraction are completed.The results indicate a 99.6%passing rate for point cloud coplanarity detection,with a maximum deviation in identified orientation parameters of only 4.82°.This research provide insights for rapid acquisition of geological information,stability evaluation,and disaster prevention and mitigation for high steep slopes.

关 键 词：无人机贴近摄影 高陡边坡 共面性检测 三维重建 结构面 

分 类 号：P694[天文地球—地质学]