Three-dimensional reconstruction and structural surface identification of high steep slopes based on UAV close-range photogrammetry
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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。
Abstract: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.
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表 1 六面体点云表面产状
Table 1. Hexahedral point cloud surface orientations
序号 倾向/（°） 倾角/（°） 点数/个 1 0 45 1 326 2 125 60 1 326 3 235 60 1 326 
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表 2 点云数据平面参数a、b、c、d的计算结果（部分）
Table 2. Calculation results for point cloud plane parameters a, b, c, d (partial)
序号 a b c d 1 0.051252697 − 0.54764324 0.83514071 664.38898 2 0.051252544 − 0.54764354 0.83514059 664.85925 ┇ ┇ ┇ ┇ ┇ 1879 0.50000018 0.70710671 − 0.50000000 500.00012 1880 0.50000006 0.70710665 − 0.50000000 500.00006 ┇ ┇ ┇ ┇ ┇ 2525 0.70710677 −1.0955361e-9 0.70710683 500 2526 0.70710671 1.5848286e-8 0.70710689 499.99994 ┇ ┇ ┇ ┇ ┇
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表 3 六面体点云表面产状与聚类结果产状对比
Table 3. Comparison of hexahedral point cloud surface occurrence with clustering result occurrence
序号 聚类中心 倾向/（°） 差值/（°） 倾角/（°） 差值/（°） 1 已知 0 0.09 45 0.19 聚类结果 0.09 45.19 2 已知 125 0.08 60 0.30 聚类结果 124.92 60.30 3 已知 235 0.2 60 0.69 聚类结果 234.80 60.69
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表 4 结构面产状信息
Table 4. Discontinuity plane orientations information
结构面 识别倾向/（°） 转换后倾向/（°） 倾角/（°） J1 273.55 176.45 5.10 J2 119.82 330.18 88.36 J3 261.47 188.53 67.67 J4 239.14 210.86 89.05 J5 27.43 62.57 50.30
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表 5 结构面产状识别
Table 5. Discontinuity plane orientation identification
分组 识别结构面 倾向/（°） 倾向差值/（°） 倾角/（°） 倾角差值/（°） 陡倾结构面1 实测产状 210 0.86 89 0.05 识别产状 210.86 89.05 陡倾结构面2 实测产状 325 4.82 89 0.64 识别产状 330.18 88.36
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