机载LiDAR点云数据的二面角滤波算法

doi:10.12082/dqxxkx.2018.170489

地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (4): 414-421.doi: 10.12082/dqxxkx.2018.170489

• 全国激光雷达大会特约稿件 • 上一篇下一篇

机载LiDAR点云数据的二面角滤波算法

刘凯斯(), 王彦兵^*(), 宫辉力, 李小娟, 余洁

1. 首都师范大学三维信息获取与应用教育部重点实验室,北京 100048
2. 首都师范大学城市环境过程与数字模拟国家重点实验室培育基地,北京 100048
3. 首都师范大学资源环境与旅游学院,北京 100048

收稿日期:2017-10-17 修回日期:2018-02-28 出版日期:2018-04-20 发布日期:2018-04-20
作者简介:
作者简介：刘凯斯（1989-）,女,博士生,研究方向为基于LiDAR点云数据的三维空间建模与分析。E-mail: 994761911@qq.com
基金资助:
国家自然科学基金面上项目（41671417）;北京市教委科技计划一般项目（KM201610028013）

Dihedral Angle Filtering Algorithm for Airborne LiDAR Point Cloud Data

LIU Kaisi(), WANG Yanbing^*(), GONG Huili, LI Xiaojuan, YU Jie

1. Key Lab of 3D Information Acquisition and Application, Ministry of Education, Beijing 100048, China
2. The State Key Laboratory Breeding Base of Processing of Urban Environment and Digital Simulation, Beijing 100048, China
3. College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China

Received:2017-10-17 Revised:2018-02-28 Online:2018-04-20 Published:2018-04-20
Contact: WANG Yanbing
Supported by:
National Natural Science Foundation of China, No.41671417;General Project of Science and Technology Plan of Beijing Municipal Education Committee, No.KM201610028013.

摘要/Abstract

摘要：

机载LiDAR是获取地表DEM的重要技术之一。本文针对机载LiDAR点云数据在复杂城区环境下的大型建筑及低矮地物滤波问题,提出一种新的二面角滤波法。利用空间二面角的平面角可以表达空间两相交平面相对位置的原理,实现机载LiDAR点云数据滤波。首先,算法提取点云数据中的高程突变点,以非突变点的二面角余弦均值稳定性作为判定迭代结束的条件;其次,分别统计高程突变和非突变点集的二面角余弦值频率分布,以交点处对应余弦值和最后一次迭代的坡度值作为LiDAR点云滤波的判定条件;最后,利用数学形态学“开”算子,去除残留低矮植被,得到可靠的滤波结果。对同一区域机载LiDAR点云数据,通过“二面角法”与“渐进三角网法”进行滤波处理。实验结果表明,二面角滤波法能有效地降低地物点错分为地面点的百分率,且在去除地物信息的同时能良好地保留地形特征。

关键词: 机载LiDAR, 二面角滤波法, 坡度, 开算子, DEM

Abstract:

Airborne LiDAR is one kind of the technologies for obtaining ground surface DEM. On the analysis of the airborne LiDAR point cloud filtering algorithms, this paper proposes a new filtering algorithm-dihedral filtering. The algorithm is based on the theory that can express the relative position of two intersect planes in space, to achieve the airborne LiDAR point cloud data filtering process. Firstly, the elevation-mutate points are extracted from point cloud data. The iteration ends when the stability of the cosine of non-mutated points′ dihedral angle reaches required level. Then, the frequency distributions of the cosine of both mutated and non-mutated points′ dihedral angle are counted, and draws a line chart. Ground points and non-ground points are classified based on the intersection′s cosine of line chart and slope value of the last iteration. Finally, the open operator of the mathematical morphology is used to remove low vegetation, and the reliable results are obtained. Comparing with ′Progressive TIN Method′, the misjudged percentage of the non-ground points were effectively reduced. Dihedral method can retain topographical information while filtering terrestrial object information.

Key words: airborne LiDAR, dihedral angle filtering, slope, open operator, DEM

刘凯斯, 王彦兵, 宫辉力, 李小娟, 余洁. 机载LiDAR点云数据的二面角滤波算法[J]. 地球信息科学学报, 2018, 20(4): 414-421.DOI:10.12082/dqxxkx.2018.170489

LIU Kaisi,WANG Yanbing,GONG Huili,LI Xiaojuan,YU Jie. Dihedral Angle Filtering Algorithm for Airborne LiDAR Point Cloud Data[J]. Journal of Geo-information Science, 2018, 20(4): 414-421.DOI:10.12082/dqxxkx.2018.170489

图/表 12

图1

图2

图3

表1

图4

图5

图6

图7

表2

图8

图9

图10

参考文献 22

[1]	惠振阳,胡友健.基于LiDAR数字高程模型构建的数学形态学滤波方法综述[J].激光与光电子学进展,2016,53(08001):1-7.
	[ Hui Z Y, Hu Y J.Review on morphological filtering algorithms based om LiDAR digital elevation model construction[J]. Laser & Optoelectronics Progress, 2016,53(08001):1-7. ]
[2]	Ackermann F.Airborne laser scanning-present status and future expectations[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 1999,54(2-3):64-67. doi: 10.1016/S0924-2716(99)00009-X
[3]	Baltsavias E.Airborne laser scanning: Existing systems and firms and other resources[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 1999,54(2-3):164-198. doi: 10.1016/S0924-2716(99)00016-7
[4]	隋立春,杨耘.基于car(p,q)模型和数学形态学理论的LiDAR点云数据滤波[J].测绘学报,2012,41(2):219-224.
	[ Sui L C, YangY. Filtering of airborne LiDAR point cloud data based on car(p,q) model and mathematical morphology[J]. Acta Geodaetica et Cartographica Sinica, 2012,41(2):219-224. ]
[5]	张小红. 机载激光雷达测量技术理论与方法[M].武汉:武汉大学出版社,2007.
	[ Zhang X H.Theory and Method of Airborne Lidar Measurement Technology[M]. WuHan: Wuhan University Press, 2007. ]
[6]	门林杰,潘中华,张阳阳,等.基于改进的坡度滤波算法的LiDAR数据滤波[J].测绘信息与工程,2011,36(2):4-5.
	[ Men L J, Pan Z H, Zhang Y Y, et al.Filtering of LiDAR data based on modified slope filtering algorithm[J]. Journal of Geomatics, 2011,36(2):4-5. ]
[7]	黄作维,刘峰,胡光伟.基于多尺度虚拟网格的LiDAR点云数据滤波改进方法[J].光学学报,2017,37(8):2-10.
	[ Huang Z W, Liu F, Hu G W.Improved method for LiDAR point cloud filtering based on hierarchical pseudo-grid[J]. Acta Optica Sinica, 2017,37(8):2-10. ]
[8]	Petzold B, Axelsson P.Result of the OEEPEWG on laser data acquisition[J]. International Archives of Photogrammetry and Remote Sensing, Amsterdam, 2000,33(B3):718-723.
[9]	Sui L C.Analysis of laser scanner data by means of digital image processing techniques[M]. Munich: Publishing House, 2003.
[10]	乔淑荣. 考虑区域增长与数学形态学的LiDAR多次回波数据滤波[J].工程勘察,2016(7):44-48.
	[ Qiao S R.Filtering multiple returns of LiDAR data based on region growing and mathematical morphology[J]. Geotechnical Investigation & Surveying, 2016(7):44-48. ]
[11]	Axelsson P.DEM generation from laser scanner data using adaptive TIN models[J]. International Archives of Photogrammetry and Remote Sensing, 2001,33(B4):110-117.
[12]	邢旭东,王星晨,吕现福.基于高程突变TIN的改进机载LiDAR点云滤波算法[J].测绘与空间地理信息,2015,38(12):105-110.
	[ Xing X D, Wang X C, Lv X F.Improved TIN filtering based on height jump for LiDAR piont clouds date[J]. Geomatics & Spatial Information Technology, 2015,38(12):105-110. ]
[13]	Yang B S, Huang R, Dong Z, 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. doi: 10.1016/j.isprsjprs.2016.07.002
[14]	周晓明,马秋禾,李二森,等.基于小波多分辨率方向高程限差的LiDAR数据滤波[J].测绘科学,2010,35(4):91-93.
	[ Zhou X M, Ma Q H, Lie E S, et al.Filter of LiDAR data based on wavelet multi-resolution and directional height tolerance[J]. Science of Surveying and Mapping, 2010,35(4):91-93. ]
[15]	王植,吴立新,贺正雄,等.基于正交多项式的平原城区机载LiDAR数据滤波算法[J].地理与地理信息科学,2012,28(1):43-46.
	[ Wang Z, Wu L X, He Z X, et al.Orthogonal polynomial filtering algorithm for plain urban airborne LiDAR data[J]. Geography and Geo-Information Science, 2012,28(1):43-46. ]
[16]	张杰,刘沂轩,孙蒙,等.基于多分辨率层次分类的机载LiDAR点云滤波方法[J].测绘科学技术学报,2017,34(1):64-69.
	[ Zhang J, Liu Y X.Airborne LiDAR data filtering method based on multi-resolution hierarchical classification[J]. Journal of Geomatics Science and Technology, 2017,34(1):64-69. ]
[17]	Weidner U, Forstner W.Towards automatic building extraction form high resolution digital elevation models[J]. International Archives of Photogrammetry and Remote Sensing, 1995,50:38-49. doi: 10.1016/0924-2716(95)98236-S
[18]	Vosselman G.Slope based filtering of laser altimetry data[J]. International Archives of Photogrammetry and Remote Sensing, 2000,33(Part B3):935-942.
[19]	Sithole G, Vosselman G.Experimental comparison of filter algorithms for bare-earth extraction from airborne laser scanning point clouds[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2004,59(1-2):85-101. doi: 10.1016/j.isprsjprs.2004.05.004
[20]	Yi C S, Liang C C.Automated searching of ground points from airborne Lidar data using a climbing and sliding method[J]. Photogrammetric Engineering and Remote Sensing, 2008,74(5):625-635. doi: 10.14358/PERS.74.5.625
[21]	张宁宁,杨英宝,于双.基于坡度和区域生长的城市LiDAR点云滤波方法[J].地理空间信息,2016,16(3):30-32. doi: 10.3969/j.issn.1672-4623.2016.03.010
	[ Zhang N N, Yang Y B, Yu S.Filtering method of urban LiDAR point cloud based on slope and region growing algorithm[J]. Geospatial Information, 2016,16(3):30-32. ] doi: 10.3969/j.issn.1672-4623.2016.03.010
[22]	胡举,杨辽,沈金祥,等.一种基于分割的机载LiDAR点云数据滤波[J].武汉大学学报·信息科学版,2012,37(3):318-321.
	[ Hu J, Yang L, Shen J X, et al.Filtering of LiDAR based on segmentation[J]. Geomatics and Information Science of Wuhan University, 2012,37(3):318-321. ]

	迭代次数/次
	1	2	3	4	5
斜率阈值	3.3	2.5	2	1.6	1.3
二面角余弦均值	0.909	0.917	0.925	0.929	0.932

滤波结果误差统计	实验区1			实验区2
滤波结果误差统计	二面角滤波法		渐进三角网法	二面角滤波法	渐进三角网法
a	678	3518		1583	7961
b	28 171	144 296		63 298	324 108
c	760	4824		1686	10 840
d	25 761	131 075		58916	294 547
Type I Error/%	2.35	2.38		2.43	2.4
Type II Error/%	2.87	3.55		2.78	3.55
Total Error/%	2.60	2.94		2.61	2.95

机载LiDAR点云数据的二面角滤波算法

Dihedral Angle Filtering Algorithm for Airborne LiDAR Point Cloud Data

RichHTML

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 12

参考文献 22

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	戚梦, 陈楠, 林偲蔚, 周千千. 引入集水区复杂网络的中国地貌识别研究[J]. 地球信息科学学报, 2023, 25(5): 909-923.
[2]	谢静, 陈楠, 林偲蔚. 基于地形音乐的地形定量分析与空间分异研究—以陕北黄土高原为例[J]. 地球信息科学学报, 2023, 25(5): 924-934.
[3]	焦怀瑾, 陈崇成, 黄洪宇. 结合ICESat-2和GEDI的中国东南丘陵地区ASTER GDEM高程精度评价与修正[J]. 地球信息科学学报, 2023, 25(2): 409-420.
[4]	师长兴. 地形扫描数据TIN模型生成DEM的误差计算方法[J]. 地球信息科学学报, 2023, 25(1): 40-48.
[5]	伍跃飞, 李建微, 毕胜, 朱馨, 王前锋. 面向山地徒步应急救援路径规划的改进蚁群算法研究[J]. 地球信息科学学报, 2023, 25(1): 90-101.
[6]	章超钦, 薛丰昌, 陈笑娟, 李婷, 韩宇, 江健, 汤伟干. 基于当量距离算法的山洪灾害避灾路线研究[J]. 地球信息科学学报, 2022, 24(5): 864-874.
[7]	肖坤, 艾廷华, 王璐. 正六边形格网DEM下的等高线生成算法及质量评价[J]. 地球信息科学学报, 2022, 24(4): 643-656.
[8]	林偲蔚, 陈楠, 刘奇祺, 贺卓文. 基于DEM小流域复杂网络的黄土高原地貌自动识别研究[J]. 地球信息科学学报, 2022, 24(4): 657-672.
[9]	代文, 陈凯, 王春, 李敏, 陶宇. 顾及DEM误差空间自相关的地形变化检测方法[J]. 地球信息科学学报, 2022, 24(12): 2297-2308.
[10]	扈常钰, 徐月雪, 宋词, 朱红春. DEM起伏纹理的视觉认知内涵与多变量消隐方法[J]. 地球信息科学学报, 2022, 24(11): 2115-2127.
[11]	刘亚坤, 李永强, 刘会云, 孙渡, 赵上斌. 基于改进RANSAC算法的复杂建筑物屋顶点云分割[J]. 地球信息科学学报, 2021, 23(8): 1497-1507.
[12]	蒋圣, 汤国安, 杨昕, 熊礼阳, 钱程扬. 基于DEM的黄土高原地形纹理概念模型[J]. 地球信息科学学报, 2021, 23(6): 959-968.
[13]	杨帅, 杨娜, 陈传法, 常兵涛, 高原, 郑婷婷. 顾及数据配准的江西省SRTM DEM精度评价和修正[J]. 地球信息科学学报, 2021, 23(5): 869-881.
[14]	江岭, 高辰, 韩枭, 孙亚婕, 赵明伟, 杨灿灿. 面向多分辨率DEM的河网相似性测度与分析[J]. 地球信息科学学报, 2021, 23(4): 576-583.
[15]	於佳宁, 刘凯, 张冰玥, 黄滢, 范晨雨, 宋春桥, 汤国安. 中国区域TanDEM-X 90 m DEM高程精度评价及其适用性分析[J]. 地球信息科学学报, 2021, 23(4): 646-657.