镜像结构和强度特征约束的海岸带LiDAR点云多路径负异常点去除方法

doi:10.11947/j.AGCS.2025.20240242

测绘学报 ›› 2025, Vol. 54 ›› Issue (5): 840-852.doi: 10.11947/j.AGCS.2025.20240242

镜像结构和强度特征约束的海岸带LiDAR点云多路径负异常点去除方法

高浩龙¹^,²(), 李邵波¹^,²^,³(), 赵建虎⁴^,⁵

^1.中国地质大学（武汉）地理与信息工程学院，湖北　武汉　430078
^2.中国地质大学（武汉）区域生态过程与环境演变湖北省重点实验室，湖北　武汉　430074
^3.自然资源部海底科学重点实验室，浙江　杭州　310012
^4.武汉大学测绘学院，湖北　武汉　430079
^5.武汉大学海洋研究院，湖北　武汉　430079

收稿日期:2024-06-17 修回日期:2025-03-27 出版日期:2025-06-23 发布日期:2025-06-23
通讯作者: 李邵波 E-mail:1202321101@cug.edu.cn;lishaobo@cug.edu.cn
作者简介:高浩龙（2001—），男，硕士生，研究方向为海洋测绘。E-mail：1202321101@cug.edu.cn
基金资助:
国家自然科学基金(42304049);湖北省自然科学基金(2023AFB017);中国博士后科学基金(2023M743282);自然资源部海洋环境探测技术与应用重点实验室开放基金(MESTA-2022-B007);自然资源部海底科学重点实验室开放基金(KLSG2406)

Multipath negative outlier removal method for coastal LiDAR point clouds based on mirror structure and intensity feature constraints

Haolong GAO¹^,²(), Shaobo LI¹^,²^,³(), Jianhu ZHAO⁴^,⁵

^1.School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China
^2.Hubei Key Laboratory of Regional Ecology and Environmental Change, China University of Geosciences, Wuhan 430074, China
^3.Key Laboratory of Submarine Geosciences, Ministry of Natural Resources, Hangzhou 310012, China
^4.School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
^5.Institute of Marine Science and Technology, Wuhan University, Wuhan 430079, China

Received:2024-06-17 Revised:2025-03-27 Online:2025-06-23 Published:2025-06-23
Contact: Shaobo LI E-mail:1202321101@cug.edu.cn;lishaobo@cug.edu.cn
About author:GAO Haolong (2001—), male, postgraduate, majors in marine surveying and mapping.　E-mail: 1202321101@cug.edu.cn
Supported by:
The National Natural Science Foundation of China(42304049);Hubei Provincial Natural Science Foundation of China(2023AFB017);China Postdoctoral Science Foundation(2023M743282);The Key Laboratory of Marine Environmental Detection Technology and Application, Ministry of Natural Resources(MESTA-2022-B007);The Key Laboratory of Submarine Geosciences, Ministry of Natural Resources(KLSG2406)

摘要/Abstract

摘要：

气垫船载激光雷达扫描系统可以实现海岸带点云数据获取，但其数据质量受多路径效应引起的负异常点影响，限制了后续应用。本文针对该类异常点的形成机制，提出了一种基于镜像结构和强度特征约束的多路径负异常点去除方法。该方法首先通过区域生长和二次最小二乘拟合获取镜像面；然后，对区域生长得到的点云簇进行几何结构和强度特征的描述；最后，基于镜像面，在镜像空间内建立点云簇的几何结构和强度特征的对应关系，从而识别并剔除具有镜像特征的负异常点。试验结果显示，本文方法在去除海岸带滩涂地形中的负异常点时，精准率和召回率分别达到了90.83%和76.05%，有效实现了负异常点去除，并在较大程度上保留了地形特征，在需要高精度去噪的场景中，提升了后续数据应用的可靠性。

关键词: 海岸带地形, LiDAR, 多路径效应, 负异常点去除, 镜像关系

Abstract:

The hovercraft-mounted LiDAR scanning system can acquire coastal zone point cloud data. However, the quality of the data is affected by negative outliers caused by the multipath effects, which limits its wide applications. This paper studied the formation mechanism of such outliers and proposed a negative outlier removal method based on mirror structure and intensity feature constraints. First, a mirror surface was obtained using the region growing and quadratic least squares fitting method. The geometric structure and intensity features of the point cloud clusters identified through region growing were then characterized. After that, using the mirror surface, the relationship of the geometric and intensity features of the point cloud clusters within the same mirror space was established. This relationship facilitated the identification and removal of negative outliers exhibiting mirror characteristics. Experimental results showed that the proposed method achieved a precision of 90.83% and a recall of 76.05% in removing negative outliers from coastal tidal flats. The method can effectively eliminate negative outliers while preserving terrain features, thereby enhancing the reliability of point cloud data for high-precision noise reduction in subsequent applications.

Key words: coastal terrain, LiDAR, multipath effects, negative outlier removal, mirror relationship

中图分类号:

P229

高浩龙, 李邵波, 赵建虎. 镜像结构和强度特征约束的海岸带LiDAR点云多路径负异常点去除方法[J]. 测绘学报, 2025, 54(5): 840-852.

Haolong GAO, Shaobo LI, Jianhu ZHAO. Multipath negative outlier removal method for coastal LiDAR point clouds based on mirror structure and intensity feature constraints[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(5): 840-852.

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参考文献 41

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设备	系统性能指标	精度范围
RA-0300激光扫描仪	测距范围/m	1.5～600
	最大脉冲频率/kHz	1000
	扫描频率/（lines/s）	10～200
	波束开角/mrad	<0.35
	测角分辨率/（°）	0.001
	测角精度/（°）	0.005
	测距精度/（mm@100 m）	5～8
	回波模式	多回波
	激光波长/nm	1550
APPLANIX	GNSS定位精度/m	0.05～0.3
POS AV610	方位角/（°）	0.005
CH-4气垫船	重量/kg	380±50
CH-4气垫船	航行速度/（km/h）	30～45

算法	测区	负异常点	正常点	TP	FP	FN	Precision/（%）	Recall/（%）	F₁值/（%）
BCSF	区域一	2157	196 332	1690	467	1574	78.35	51.78	62.35
BCSF	区域二	3632	60 318	3439	193	149	94.69	95.85	95.26
RandLA-Net	区域一	2330	196 159	1336	994	1928	57.34	40.93	47.77
RandLA-Net	区域二	3871	60 079	2923	948	665	75.51	81.47	78.38
本文算法	区域一	2692	195 797	2148	544	1116	79.79	65.81	72.13
本文算法	区域二	3738	60 212	3524	214	64	94.28	98.22	96.21

算法	Precision/（%）	Recall/（%）	F₁值/（%）	计算用时
BCSF	58.35	72.07	64.50	57 min 46 s
RandLA-Net	45.42	42.16	43.73	3 min 11 s
本文算法	90.83	76.05	82.79	17 min 37 s

镜像结构和强度特征约束的海岸带LiDAR点云多路径负异常点去除方法

Multipath negative outlier removal method for coastal LiDAR point clouds based on mirror structure and intensity feature constraints

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