基于地形断裂线约束的机载激光点云高精度滤波方法

doi:10.11947/j.AGCS.2023.20220616

测绘学报 ›› 2023, Vol. 52 ›› Issue (12): 2164-2177.doi: 10.11947/j.AGCS.2023.20220616

基于地形断裂线约束的机载激光点云高精度滤波方法

杨宇妍¹, 臧玉府¹, 肖雄武², 管海燕¹, 彭代峰¹

1. 南京信息工程大学遥感与测绘工程学院, 江苏南京 210044;
2. 武汉大学测绘遥感信息工程国家重点实验室, 湖北武汉 430079

收稿日期:2022-10-28 修回日期:2023-06-12 发布日期:2024-01-03
通讯作者: 臧玉府 E-mail:3dmapzangyufu@nuist.edu.cn
作者简介:杨宇妍(1999-),女,硕士生,研究方向为三维点云智能处理。E-mail:YangYuyan7768@163.com
基金资助:
国家自然科学基金(42171433;41701529);江苏省高等学校自然科学研究项目(17KJB420004)

An accurate breakline-aware filtering method for airborne laser scanning point clouds

YANG Yuyan¹, ZANG Yufu¹, XIAO Xiongwu², GUAN Haiyan¹, PENG Daifeng¹

1. School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China;
2. State Key Laboratory of Information Engineering in Surveying, Mapping & Remote Sensing, Wuhan University, Wuhan 430079, China

Received:2022-10-28 Revised:2023-06-12 Published:2024-01-03
Supported by:
The National Natural Science Foundation of China (Nos. 42171433;41701529);The Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 17KJB420004)

摘要/Abstract

摘要： 现有滤波方法在地形复杂、高程突变等区域往往会错误滤除大量地面点,降低了滤波结果的准确性且严重影响了后续应用。为此,本文提出一种融合断裂线约束模型的采样线高效滤波方法。首先,基于点曲率及邻域范围内距离之和创建约束能量项,改进Snake能量模型,并通过能量函数最小化提取断裂线特征;然后,从测区中获取点云序列等间距构成采样线,构建正三角模型、计算点云平坦度融合断裂线约束过滤每条采样线上的点;最后,根据采样线上的地面点通过改进最小二乘法拟合全局曲面,并结合其实际高程值过滤区域非地面点。为验证本文方法的有效性,本文采用5份不同区域场景的机载激光点云数据进行试验分析。结果表明,本文方法在效率和精度方面优于参照方法,平均滤波精度高达96.58%,尤其在地形断裂区域处滤波效果显著。

关键词: 机载点云, 采样线滤波, 断裂线约束, 局部曲面拟合

Abstract: The existing filtering methods often mistakenly filter a large number of ground points in areas with complex terrain and abrupt elevation, which reduces the accuracy of filtering results and seriously affects the subsequent application. For this reason, this paper proposes an efficient filtering method for sampling lines by fusing the breakline constraint model. Firstly, a constraint energy term is created based on the sum of point curvature and distance in the neighborhood, and the Snake energy model is improved, and features of breaklines are extracted by minimizing the energy function. Then, the point cloud sequence is obtained from the survey area to form a sampling line at equal intervals, and the regular triangle model is constructed, the flatness of point clouds is calculated, and the points on each sampling line are filtered by fusing the breakline constraint. Finally, according to ground points on the sampling line, the improved least square method is used to fit global surface, and the non ground points in the region are filtered according to their actual elevation values. In order to verify the effectiveness of this method, this paper uses five airborne laser point cloud data from different regional scenes for experimental analysis. The results show that the proposed method is better than the reference method in terms of efficiency and accuracy, and the average filtering accuracy is as high as 96.58%, especially in the terrain fault area.

Key words: airborne point clouds, sampling line filtering, breaklines constraint, local surface fitting

中图分类号:

P237

杨宇妍, 臧玉府, 肖雄武, 管海燕, 彭代峰. 基于地形断裂线约束的机载激光点云高精度滤波方法[J]. 测绘学报, 2023, 52(12): 2164-2177.

YANG Yuyan, ZANG Yufu, XIAO Xiongwu, GUAN Haiyan, PENG Daifeng. An accurate breakline-aware filtering method for airborne laser scanning point clouds[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(12): 2164-2177.

参考文献

[1] HU Han, DING Yulin, ZHU Qing, et al. An adaptive surface filter for airborne laser scanning point clouds by means of regularization and bending energy[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 92: 98-111.
[2] YANG Bisheng, HUANG Ronggang, DONG Zhen, et al. Two-step adaptive extraction method for ground points and breaklines from LiDAR point clouds[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 119: 373-389.
[3] 宿殿鹏, 闫豆豆, 陈亮, 等. 机载LiDAR测深点云SVB联合滤波算法[J]. 测绘学报, 2023, 52(4): 614-623. DOI: 10.11947/j.AGCS.2023.20220248. SU Dianpeng, YAN Doudou, CHEN Liang, et al. Surface-volume-bottom joint-filtering algorithm for airborne LiDAR bathymetric point cloud[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(4): 614-623. DOI: 10.11947/j.AGCS.2023.20220248.
[4] 汪文琪, 李宗春, 付永健, 等. 一种多尺度自适应点云坡度滤波算法[J]. 武汉大学学报(信息科学版), 2022, 47(3): 438-446. WANG Wenqi, LI Zongchun, FU Yongjian, et al. A multi-scale adaptive slope filtering algorithm for point cloud[J]. Geomatics and Information Science of Wuhan University, 2022, 47(3): 438-446.
[5] SUSAKI J. Adaptive slope filtering of airborne LiDAR data in urban areas for digital terrain model (DTM) generation[J]. Remote Sensing, 2012, 4(6): 1804-1819.
[6] VOSSELMAN G. Slope based filtering of laser altimetry data[EB/OL].[2022-10-28]. https://www.researchgate.net/publication/228719860_Slope_based_filtering_of_laser_altimetry_data.
[7] LI Y, YONG B, VAN OOSTEROM P, et al. Airborne LiDAR data filtering based on geodesic transformations of mathematical morphology[J]. Remote Sensing, 2017, 9(11): 1104.
[8] MONGUS D, LUKAČ N, ŽALIK B. Ground and building extraction from LiDAR data based on differential morphological profiles and locally fitted surfaces[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 93: 145-156.
[9] HUI Zhenyang, HU Youjian, YEVENYO Y, et al. An improved morphological algorithm for filtering airborne LiDAR point cloud based on multi-level Kriging interpolation[J]. Remote Sensing, 2016, 8(1): 35.
[10] ZHAO Dineng, WU Ziyin, ZHOU Jieqiong, et al. Parameter group optimization by combining CUBE with surface filtering and its application[J]. Journal of Geodesy and Geoinformation Science, 2020, 3(2): 81-92.
[11] LIN Xiangguo, ZHANG Jixian. Segmentation-based filtering of airborne LiDAR point clouds by progressive densification of terrain segments[J]. Remote Sensing, 2014, 6(2): 1294-1326.
[12] AXELSSON P. DEM generation from laser scanner data using adaptive TIN models[EB/OL]. [2022-10-28]. https://www.isprs.org/proceedings/XXXIII/congress/part4/111_XXXIII-part4.pdf?origin=publication_detail.
[13] CHEN Qi, WANG Huan, ZHANG Hanchao, et al. A point cloud filtering approach to generating DTMs for steep mountainous areas and adjacent residential areas[J]. Remote Sensing, 2016, 8(1): 71.
[14] ZHANG Wuming, QI Jianbo, WAN Peng, et al. An easy-to-use airborne LiDAR data filtering method based on cloth simulation[J]. Remote Sensing, 2016, 8(6): 501.
[15] YIN Huilin, YANG Xiaohan, HE Chao. Spherical coordinates based methods of ground extraction and objects segmentation using 3D LiDAR sensor[J]. IEEE Intelligent Transportation Systems Magazine, 2016, 8(1): 61-68.
[16] 郑辑涛, 张涛. 基于可变半径圆环和B样条拟合的机载LiDAR点云滤波[J]. 测绘学报, 2015, 44(12): 1359-1366. ZHENG Jitao, ZHANG Tao. Filtering of airborne LiDAR point cloud based on variable radius circle and B-spline fitting[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(12): 1359-1366.
[17] SÁNCHEZ J M,ÁLVAREZ Á V, VILARIÑO D L, et al. Fast ground filtering of airborne LiDAR data based on iterative scan-line spline interpolation[J]. Remote Sensing, 2019, 11(19): 2256.
[18] KASS M, WITKIN A, TERZOPOULOS D. Snakes: active contour models[J]. International Journal of Computer Vision, 1988, 1(4): 321-331.
[19] 贺美芳, 周来水, 神会存. 散乱点云数据的曲率估算及应用[J]. 南京航空航天大学学报, 2005, 37(4): 515-519. HE Meifang, ZHOU Laishui, SHEN Huicun. Curvature estimation of scattered-point cloud data and its application[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2005, 37(4): 515-519.
[20] ROUL P, PRASAD GOURA V M K. B-spline collocation methods and their convergence for a class of nonlinear derivative dependent singular boundary value problems[J]. Applied Mathematics and Computation, 2019, 341: 428-450.
[21] ABDI H, WILLIAMS L J. Principal component analysis[J]. WIREs Computational Statistics, 2010, 2(4): 433-459.
[22] 方莉娜, 卢丽靖, 赵志远, 等. 车载激光点云道路边界提取的Snake方法[J]. 测绘学报, 2020, 49(11): 1438-1450. DOI: 10.11947/j.AGCS.2020.20190370. FANG Lina, LU Lijing, ZHAO Zhiyuan, et al. Road boundaries extraction from mobile laser scanning point clouds based on discrete point Snake[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(11): 1438-1450. DOI: 10.11947/j.AGCS.2020.20190370.
[23] 张志超. 融合机载与地面LIDAR数据的建筑物三维重建研究[D]. 武汉: 武汉大学, 2010. ZHANG Zhichao. Airborne and terrestrial LiDAR data fusion for 3D building reconstruction[D]. Wuhan: Wuhan University, 2010.
[24] 赵传, 郭海涛, 卢俊, 等. 结合区域增长与RANSAC的机载LiDAR点云屋顶面分割[J]. 测绘学报, 2021, 50(5): 621-633. DOI: 10.11947/j.AGCS.2021.20200270. ZHAO Chuan, GUO Haitao, LU Jun, et al. Roof segmentation from airborne LiDAR by combining region growing with random sample consensus[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(5): 621-633. DOI: 10.11947/j.AGCS.2021.20200270.
[25] 郭杰, 刘建永, 张有亮, 等. 基于扫描线自适应角度限差法的地面点云滤波[J]. 计算机应用, 2011, 31(8): 2243-2245. GUO Jie, LIU Jianyong, ZHANG Youliang, et al. Filtering of ground point cloud based on scanning line and self-adaptive angle-limitation algorithm[J]. Journal of Computer Applications, 2011, 31(8): 2243-2245.
[26] 詹总谦, 胡孟琦, 满益云. 多尺度区域生长点云滤波地表拟合法[J]. 测绘学报, 2020, 49(6): 757-766. DOI: 10.11947/j.AGCS.2020.20190142. ZHAN Zongqian, HU Mengqi, MAN Yiyun. Multi-scale region growing point cloud filtering method based on surface fitting[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(6): 757-766. DOI: 10.11947/j.AGCS.2020.20190142.
[27] SITHOLE G. Filtering of laser altimetry data using a slope adaptive filter[EB/OL].[2022-10-28]. https://www.isprs.org/proceedings/XXXIV/3-W4/pdf/Sithole.pdf.
[28] BROVELLI M, CANNATA M, LONGONI U. Managing and processing LiDAR data within GRASS[C]// Proceedings of 2002 Open Source GIS-GRASS Users Conference. Trento:[s.n.], 2002: 1-29.
[29] ZHANG Jixian, LIN Xiangguo. Filtering airborne LiDAR data by embedding smoothness-constrained segmentation in progressive TIN densification[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 81: 44-59.

基于地形断裂线约束的机载激光点云高精度滤波方法

An accurate breakline-aware filtering method for airborne laser scanning point clouds

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	蔡龙涛. 国产星载激光雷达全链路森林回波仿真及森林结构参数反演研究[J]. 测绘学报, 2023, 52(12): 2223-2223.
[2]	程结海, 黄中意, 王建如, 何湜. 高空间分辨率遥感影像最优分割结果自动确定方法[J]. 测绘学报, 2022, 51(5): 658-667.
[3]	梁哲恒, 黎宵, 邓鹏, 盛森, 姜福泉. 融合多尺度特征注意力的遥感影像变化检测方法[J]. 测绘学报, 2022, 51(5): 668-676.
[4]	白坤, 慕晓冬, 陈雪冰, 朱永清, 尤轩昂. 融合半监督学习的无监督遥感影像场景分类[J]. 测绘学报, 2022, 51(5): 691-702.
[5]	黄明益, 吴军, 高炯笠. 多镜头全景摄像机球面视频无缝生成[J]. 测绘学报, 2022, 51(5): 703-717.
[6]	王丹菂, 邢帅, 徐青, 林雨准, 李鹏程. 单频机载激光测深海陆回波自动分类方法[J]. 测绘学报, 2022, 51(5): 750-761.
[7]	张志敏. 基于遥感反照率的青藏高原冰川年际物质平衡估算研究[J]. 测绘学报, 2022, 51(5): 781-781.
[8]	李永强, 李鹏鹏, 董亚涵, 范辉龙. 车载LiDAR点云数据中杆状地物自动提取与分类[J]. 测绘学报, 2020, 49(6): 724-735.
[9]	王竞雪, 刘肃艳, 王伟玺. 联合共线约束与匹配冗余的组直线匹配结果检核算法[J]. 测绘学报, 2020, 49(6): 746-756.
[10]	詹总谦, 胡孟琦, 满益云. 多尺度区域生长点云滤波地表拟合法[J]. 测绘学报, 2020, 49(6): 757-766.
[11]	韩斌, 吴一全. SAR图像河流提取的主动轮廓模型的稳健估计算法[J]. 测绘学报, 2020, 49(6): 777-786.
[12]	邓睿哲, 陈启浩, 陈奇, 刘修国. 遥感影像船舶检测的特征金字塔网络建模方法[J]. 测绘学报, 2020, 49(6): 787-797.
[13]	黄亮. 多时相遥感影像变化检测技术研究[J]. 测绘学报, 2020, 49(6): 801-801.
[14]	吴文豪, 张磊, 李陶, 龙四春, 段梦, 周志伟, 祝传广, 蒋廷臣. 基于几何配准的多模式SAR影像配准及其误差分析[J]. 测绘学报, 2019, 48(11): 1439-1451.
[15]	赵生银, 安如, 朱美如. 联合像元-深度-对象特征的遥感图像城市变化检测[J]. 测绘学报, 2019, 48(11): 1452-1463.