机载LiDAR测深点云SVB联合滤波算法

doi:10.11947/j.AGCS.2023.20220248

测绘学报 ›› 2023, Vol. 52 ›› Issue (4): 614-623.doi: 10.11947/j.AGCS.2023.20220248

机载LiDAR测深点云SVB联合滤波算法

宿殿鹏^1,2,3, 闫豆豆^1,4, 陈亮⁵, 陈雨⁵, 董箭², 吴迪², 于孝林^1,4

1. 山东科技大学测绘与空间信息学院, 山东青岛 266590;
2. 海军大连舰艇学院海洋测绘工程军队重点实验室, 辽宁大连 116018;
3. 中国科学院上海光学精密机械研究所, 上海 201800;
4. 自然资源部海洋测绘重点实验室, 山东青岛 266590;
5. 中测瑞格测量技术(北京)有限公司, 北京 100125

收稿日期:2022-04-14 修回日期:2022-08-30 发布日期:2023-05-05
通讯作者: 闫豆豆 E-mail:1398965619@qq.com
作者简介:宿殿鹏(1988-),男,博士,副教授,研究方向为机载LiDAR测深数据处理与应用。E-mail:sudianpeng@126.com
基金资助:
海洋环境保障创新开放基金(HHB004);中国博士后科学基金(2021M700155);国家自然科学基金(52001189;41930535);高端外国专家引进计划(G2021025006L);山东科技大学科研创新团队支持计划(2019TDJH103);自然资源部海洋测绘重点实验室开放基金(2021B05);青岛市关键技术攻关及产业化示范类项目(23-1-3-hygg-1-hy)

Surface-volume-bottom joint-filtering algorithm for Airborne LiDAR bathymetric point cloud

SU Dianpeng^1,2,3, YAN Doudou^1,4, CHEN Liang⁵, CHEN Yu⁵, DONG Jian², WU Di², YU Xiaolin^1,4

1. College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China;
2. Key Laboratory of Hydrographic Surveying and Mapping of PLA, Dalian Naval Academy, Dalian 116018, China;
3. Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China;
4. Key Laboratory of Ocean Geomatics, Ministry of Natural Resources of China, Qingdao 266590, China;
5. RCG Geosystems(Beijing) Co., Ltd., Beijing 100125, China

Received:2022-04-14 Revised:2022-08-30 Published:2023-05-05
Supported by:
Marine Environment Protection Innovation and Open Fund (No. HHB004);China Postdo-ctoral Science Foundation(No. 2021M700155);The National Natural Science Foundation of China (Nos. 52001189;41930535);High-end Foreign Expert Introduction Program (No. G2021025006L);Shandong University of Science and Technology Research and Innovation Team Support Program(No. 2019TDJH103);The Open Fund of Key Laboratory of Marine Surveying and Mapping, Ministry of Natural Resources (No. 2021B05);Qingdao Key technology research and industrialization demonstration projects (No. 23-1-3-hygg-1-hy)

摘要/Abstract

摘要： 机载LiDAR测深(airborne LiDAR bathymetry,ALB)数据质量受海面破碎波浪、水体浮藻、鱼群及海底二次回波等多种因素影响。为剔除这些干扰产生的噪点,本文提出一种顾及水面、水体和水底(surface,volume,bottom,SVB)的联合滤波算法。针对水面噪点,通过构建双层布料模拟滤波模型分离水面点云;针对水体噪点,采用SOR(statistical outlier removal)滤波器剔除水体离群点;针对靠近地形主体的小尺度水底噪点,通过构建移动趋势面模型进行去噪平滑。为验证本文所提ALB滤波算法的性能,采用青岛胶州湾海域RIEGL VQ-840-G无人机载LiDAR测深数据进行验证,试验结果表明:SVB联合滤波算法对水面、水体、水底噪点一体化处理总体滤波精度和Kappa系数分别能够达到97.45%和0.947,在保证准确率的同时具有较高的效率。本文所提滤波算法可以较好地解决ALB点云滤波问题,能够为ALB测深数据点云滤波提供有效的解决方案。

关键词: 机载LiDAR测深, SVB联合滤波, 双层布料模拟, SOR滤波, 移动趋势面拟合

Abstract: The data quality of airborne LiDAR bathymetry (ALB) is affected by many factors (such as sea surface fragmentation waves, floating algae, fish groups, and submarine secondary echoes). To reduce the noise generated by these interferences, a joint-filtering algorithm taking into account the surface, volume, bottom (SVB) is proposed. For water surface noise, the point cloud on the sea surface is separated by building the opposing cloth simulation filter model. Then, the water body outlier is removed by establishing a SOR (statistical outlier removal) filter. Finally, the noise smoothing is performed by building a moving trend surface model for small-scale underwater noise near the terrain body. The ALB data collected in the Jiaozhou Bay area of Qingdao using RIEGL VQ-840-G UAV on-board LiDAR bathymetric system are used to verify the performance of the proposed SVB filtering algorithm. The experimental results show that the overall accuracy and Kappa coefficient of the SVB joint-filtering algorithm can reach 97.45% and 0.947, respectively. It has high efficiency while ensuring the accuracy rate. Compared to the existing algorithms, the proposed filtering algorithm can better solve the problem of ALB point cloud filtering, and can provide an effective solution for ALB bathymetric data point cloud filtering.

Key words: airborne LiDAR bathymetry, SVB joint-filtering, double-layer cloth analog filtering, SOR filtering, mobile trend surface fitting

中图分类号:

P227

宿殿鹏, 闫豆豆, 陈亮, 陈雨, 董箭, 吴迪, 于孝林. 机载LiDAR测深点云SVB联合滤波算法[J]. 测绘学报, 2023, 52(4): 614-623.

SU Dianpeng, YAN Doudou, CHEN Liang, CHEN Yu, DONG Jian, WU Di, YU Xiaolin. Surface-volume-bottom joint-filtering algorithm for Airborne LiDAR bathymetric point cloud[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(4): 614-623.

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机载LiDAR测深点云SVB联合滤波算法

Surface-volume-bottom joint-filtering algorithm for Airborne LiDAR bathymetric point cloud

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