Information extraction of dangerous rock masses on high and steep slopes using multi-source remote sensing data fusion
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摘要:
我国山区存在大量的高陡边坡，因其具有隐蔽性及危险性等特点，目前单一非接触式测量难以获取可用于高陡边坡危岩体几何参数及结构面信息提取分析的数据，而对于危岩体精细化调查，结构面特征参数信息又是重中之重。将机载激光雷达、地面激光雷达及无人机倾斜摄影测量获取的点云数据进行了多源数据融合，运用融合后点云对高陡边坡危岩的规模边界、后缘特征和产状信息等参数进行了信息提取。结果表明：多源数据融合方法有效互补了多种数据优势，运用融合后点云对高陡边坡危岩进行了规模边界、后缘信息及结构面特征参数信息提取，其数据提取值误差均在±5°以内，满足调查规范要求。研究成果为植被覆盖区域的危岩体详细调查提供了新的思路。
Abstract:Objective There are a large number of high and steep slopes in mountainous areas in China. Due to their concealed and hazardous nature, it is currently difficult to obtain usable data for extracting and analyzing geometric parameters and structural plane information of dangerous rock masses on high and steep slopes using a single non-contact measurement method. However, for the refined investigations of dangerous rock masses, the characteristic parameter information of structural planes is a top priority.
Methods Therefore, this paper fused the point cloud data obtained from airborne LiDAR, ground LiDAR and UAV oblique photogrammetry through multi-source data, integrated the advantages of the multi-source data, and used the fused point cloud to analyze dangerous rock masses on high and steep slopes. Parameters such as scale boundaries, back edge characteristics and occurrence information were extracted.
Results The results showed that the multi-source data fusion method adopted in this study effectively integrated the advantages of various data types.The values of extracted parameters, including scale boundaries, back edge features, and structural plane characteristics, all exhibited deviations within ±5°, meeting the requirements of the survey specifications.
Conclusion These research findings provide a new approach for detailed investigations of dangerous rock masses in vegetation-covered areas.
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图 1 多源数据优劣势分析
a. 地面激光雷达立面点云完整；b. 地面激光雷达无后缘及植被区域点云；c. 无人机倾斜摄影测量获取完整地物；d. 无人机倾斜摄影测量点云缺失；e. 机载LiDAR滤除植被；f. 机载LiDAR点云缺失
Figure 1. Analysis of advantages and disadvantages of multi-source data
图 2 多源数据融合原理示意图
a. 四点一致法（4PC）点云粗配准；b. 迭代最近点法（ICP）点云精细配准；c. 点云融合；a~d, a'~d'分别为2个数据源点云公共区域的4个同名点; e, e'分别为2个数据源4个共面点的相交点
Figure 2. Schematic diagram of multi-source data fusion principle
图 3 多源数据融合及应用技术流程图
Figure 3. Flow chart of multi-source data fusion and application technology
图 4 危岩体规模体积信息及后缘信息提取
Figure 4. Extraction of scale volume information and trailing edge information of dangerous rock mass
图 6 正二十六面体分水岭算法提取结果示意图（W1～W10. 结构面编号，下同）
Figure 6. Schematic diagram of extraction results of regular icosahedron watershed algorithm
图 7 结构面特征参数信息提取示意图
Figure 7. Schematic diagram of structural surface feature parameter information extraction
图 11 危岩体后缘特征信息提取
Figure 11. Extraction of characteristic information of dangerous rock mass trailing edge
图 12 结构面部分特征参数提取图
Figure 12. Partial feature parameters extraction diagram of structural surfac
表 1 Hough空间法向量计算参数
Table 1. Hough space normal vector calculation parameters
领域尺寸
K平面数量
T累加器部署
nPhi累加器循环
次数nRot公差角
Tola/(°)领域估计密
度大小Nsde100 700 15 5 90 5 
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表 2 正二十六面体自动聚类产状与已知结果产状对比
Table 2. Comparison between automatic clustering attitude of regular icosahedron and known results attitude
结构面
编号自动识别产状/（°） 已知结果产状/（°） 误差/（°） 倾向 倾角 倾向 倾角 倾向 倾角 W1 349 69 350 70 1 1 W2 28 42 27 41 1 1 W3 66 70 66 70 0 0 W4 109 71 109 70 0 1 W5 148 40 148 41 0 1 W6 185 69 186 70 1 1 W7 230 70 230 70 0 0 W8 266 40 268 41 2 1 W9 303 69 305 70 2 1 W10 / / / / / / 平均误差值 0.8 0.8
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表 3 多源遥感数据质量参数
Table 3. Multi-source remote sensing data quality parameters
点云数据来源 点数量/个 覆盖面积/
km2最大点密度/
(点·m−2)平均点密度/
(点·m−2)无人机倾斜摄影测量 131225760 2.97 618 44.1 地面激光雷达 33980705 1.12 49584 30.4 机载LiDAR 69351093 1.85 387 37.4
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表 4 分水岭算法自动聚类提取结构面产状与人工测量结构面产状对比
Table 4. Comparison between structural surface attitude extracted by the watershed algorithm automatic clustering and structural surface attitude obtained by manual measurement
结构面
组号人工测量产状/(°) 自动识别产状/(°) 误差/(°) 倾向 倾角 倾向 倾角 倾向 倾角 J1 312.8 78.7 316.4 78.4 2.6 0.3 J2 147.3 76.5 148.1 79.8 0.8 3.3 J3 306.9 47.8 309.4 46.1 2.5 1.7 平均误差值 2.0 1.8
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