多尺度区域生长点云滤波地表拟合法

doi:10.11947/j.AGCS.2020.20190142

测绘学报 ›› 2020, Vol. 49 ›› Issue (6): 757-766.doi: 10.11947/j.AGCS.2020.20190142

多尺度区域生长点云滤波地表拟合法

詹总谦¹, 胡孟琦¹, 满益云²

1. 武汉大学测绘学院, 湖北武汉 430079;
2. 钱学森空间技术实验室, 北京 100094

收稿日期:2019-05-09 修回日期:2020-04-06 出版日期:2020-06-20 发布日期:2020-06-28
通讯作者: 胡孟琦 E-mail:humengqi＠whu.edu.cn
作者简介:詹总谦(1978-),男,副教授,研究方向为数字摄影测量与计算机视觉。E-mail:zqzhan@sg.whu.edu.cn
基金资助:
国家自然科学基金（61871295）

Multi-scale region growing point cloud filtering method based on surface fitting

ZHAN Zongqian¹, HU Mengqi¹, MAN Yiyun²

1. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China;
2. Qian Xuesen Laboratory of Space Technology, Beijing 100094, China

Received:2019-05-09 Revised:2020-04-06 Online:2020-06-20 Published:2020-06-28
Supported by:
The National Natural Science Foundation of China (No. 61871295)

摘要/Abstract

摘要： 针对经典的迭代三角网加密算法（PTD）过度侵蚀地形、误差累积的问题，提出了一种基于区域生长的多尺度滤波方法。该方法引入了金字塔策略建立不同层次的点云结构，以上层种子点为基准对下层种子点进行处理：先通过不规则三角网滤除非地面点，然后依据局部地形设置动态阈值，以表面拟合区域生长算法增长受侵蚀的地面种子点，循环迭代逐渐逼近真实地面。通过对ISPRS提供的15个基准数据集进行测试，第Ⅰ、Ⅱ类误差以及总误差分别为2.40%、3.67%、2.84%，Kappa系数为93.74%。结果表明，该算法具有更强的性能，可以获得理想的地面模型。

关键词: 点云滤波, 数字高程模型, 三角网, 数据金字塔, 区域生长

Abstract: Aiming at the problem of over-erosion and type II error accumulation by progressive triangle irregular network(TIN) densification(PTD), a multi-scale filtering method based on region growing is proposed. This method introduces pyramid strategy to establish different levels of point cloud, in which the low-level seed points are processed based on the high-level seed points. In the filtering process, the non-ground points are filtered by PTD first, and then the eroded ground seed points are compensated by the surface-fitting region growing algorithm with dynamic threshold determinated by local terrain, ultimately the real ground surface is gradually approached in loop iteration. By testing the 15 benchmark data sets provided by the ISPRS, Type I error, Type II error, Total error and Cohen’s kappa coefficient are 2.40%, 3.67%, 2.84% and 93.74% respectively, which shows that the proposed method has better performance to obtain the ideal ground model.

Key words: point cloud filtering, digital elevation model, triangle irregular network, data pyramid, region growing

中图分类号:

P237

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.

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.

参考文献 27

[1]	WEHR A, LOHR U. Airborne laser scanning-an introduction and overview[J]. ISPRS Journal of Photogram-metry and Remote Sensing, 1999, 54(2-3):68-82.
[2]	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 Xiaohong. Theory and method of airborne LiDAR measurement technology[M]. Wuhan:Wuhan University Press, 2007.
[3]	黄先锋, 李卉, 王潇, 等. 机载LiDAR数据滤波方法评述[J]. 测绘学报, 2009, 38(5):466-469. DOI:10.3321/j.issn:1001-1595.2009.05.014. HUANG Xianfeng, LI Hui, WANG Xiao, et al. Filter algorithms of airborne LiDAR data:review and prospects[J]. Acta Geodaetica et Cartographica Sinica, 2009, 38(5):466-469. DOI:10.3321/j.issn:1001-1595.2009.05.014.
[4]	杨必胜, 梁福逊, 黄荣刚. 三维激光扫描点云数据处理研究进展、挑战与趋势[J]. 测绘学报, 2017, 46(10):1509-1516. DOI:10.11947/j.AGCS.2017.20170351. YANG Bisheng, LIANG Fuxun, HUANG Ronggang. Progress, challenges and perspectives of 3D LiDAR point cloud processing[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10):1509-1516. DOI:10.11947/j.AGCS.2017.20170351.
[5]	李德仁, 王艳军, 邵振峰. 新地理信息时代的信息化测绘[J]. 武汉大学学报(信息科学版), 2012, 3(1):1-6. LI Deren, WANG Yanjun, SHAO Zhenfeng. Geo-informatization of new geographic information era[J]. Geomatics an Information Science of Wuhan University, 2012, 3(1):1-6.
[6]	惠振阳, 程朋根, 官云兰, 等. 机载LiDAR点云滤波综述[J]. 激光与光电子学进展, 2018, 55(6):060001. HUI Zhenyang, CHENG Penggen, GUAN Yunlan, et al. Review on airborne LiDAR point cloud filtering[J]. Laser & Optoelectronics Progress, 2018, 55(6):060001.
[7]	MENG Xuelian, CURRIT N, ZHAO Kaiguang. Ground filtering algorithms for airborne LiDAR data:a review of critical issues[J]. Remote Sensing, 2010, 2(3):833-860.
[8]	VOSSELMANN G. Slope-based filtering of laser altimetry data[J]. International Archives of Photogrammetry and Remote Sensing, 2000, 33(Part B3/2):935-942.
[9]	SITHOLE G. Filtering of laser altimetry data using a slope adaptive filter[J]. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 2001, 34(3/W4):203-210.
[10]	AXELSSON P. DEM generation from laser scanner data using adaptive TIN models[J]. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 2000, 33(Part B4/1):110-117.
[11]	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]. SPRS Journal of Photogrammetry and Remote Sensing, 2014, 92:98-111.
[12]	朱笑笑, 王成, 习晓环, 等. 多级移动曲面拟合的自适应阈值点云滤波方法[J]. 测绘学报, 2018, 47(2):153-160. DOI:10.11947/j.AGCS.2018.20170491. ZHU Xiaoxiao, WANG Cheng, XI Xiaohuan, et al. Hierarchical threshold adaptive for point cloud filter algorithm of moving surface fitting[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(2):153-160. DOI:10.11947/j.AGCS.2018.20170491.
[13]	CHEN Chuanfa, LI Yanyan, LI Wei, et al. A multiresolution hierarchical classification algorithm for filtering airborne LiDAR data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 82:1-9.
[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]	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. DOI:10.11947/j.AGCS.2015.20140514. 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. DOI:10.11947/j.AGCS.2015.20140514.
[16]	SOHN G, DOWMAN I J. Terrain surface reconstruction by the use of tetrahedron model with the MDL criterion[J]. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 2002, 34(3A):336-344.
[17]	ZHANG Keqi, CHEN S C, WHITMAN D, et al. A progressive morphological filter for removing nonground measurements from airborne LiDAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(4):872-882.
[18]	HUI Zhenyang, HU Youjian, YEVENYO Y. An improved morphological algorithm for filtering airborne LiDAR point cloud based on multi-level Kriging interpolation[J]. Remote Sensing, 2016, 8(1):35.
[19]	PINGEL T J, CLARKE K C, MCBRIDE W A. An improved simple morphological filter for the terrain classification of airborne LiDAR data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 77:21-30.
[20]	FILIN S. Surface clustering from airborne laser scanning data[J]. International Archives of Photogrammetry and Remote Sensing, 2002, 34(Part 3A):119-124.
[21]	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.
[22]	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.
[23]	LEE D T, SCHACHTER B J. Two algorithms for constructing a delaunay triangulation[J]. International Journal of Computer & Information Sciences, 1980, 9(3):219-242.
[24]	ZHAO Xiaoqian, GUO Qinghua, SU Yanjun, et al. Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 117:19-91.
[25]	MUJA M, LOWE D G. Fast approximate nearest neighbors with automatic algorithm configura-tion[C]//Proceedings of the 4th International Conference on Computer Vision Theory and Applications. Lisboa, Portugal:[s.n.], 2009.
[26]	PFEIFER N, STADLER P, BRIESE C. Derivation of digital terrain models in the SCOP++ environ-ment[C]//Proceedings of OEEPE Workshop on Airborne Laserscanning and Interferometric SAR for Detailed Digital Terrain Models. Stockholm:[s.n.], 2001.
[27]	CONGALTON R G. A review of assessing the accuracy of classifications of remotely sensed data[J]. Remote Sensing of Environment, 1991, 37(1):35-46.

多尺度区域生长点云滤波地表拟合法

Multi-scale region growing point cloud filtering method based on surface fitting

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