Acta Geodaetica et Cartographica Sinica >

2015 , Vol. 44 >Issue 3: 282 - 291

DOI: https://doi.org/10.11947/j.AGCS.2015.20130423

Hierarchical Outlier Detection for Point Cloud Data Using a Density Analysis Method

  • ZHU Junfeng ,
  • HU Xiangyun ,
  • ZHANG Zuxun ,
  • XIONG Xiaodong
Expand
  • 1. Chinses Academy of Surveying & Mapping, Beijing 100830, China;
    2. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

Received date: 2013-12-09

  Revised date: 2014-04-15

  Online published: 2015-04-01

Supported by

The National Natural Science Foundation of China (No.41271374);Basic Scientific Research Projects of Chinese Academy of Surveying & Mapping (No.7771402);The National Basic Research Program of China(973 Program) (No.2012CB719904);The National High-tech Research and Development Program of China(863 Program) (No.2013AA063905);Academic Award for Excellent PhD Candidates Funded by Ministry of Education of China under Grant (No.5052012213001);The Fundamental Research Founds for the Central Universities (No.2012213020207)

Fold

Abstract

Laser scanning and image matching are both effective ways to get dense point cloud data, however, outliers obtained from both ways are still inevitable. A novel hierarchical outlier detection method is proposed for the automatic outlier detection of point cloud from image matching and airborne laser scanning. There are two main steps in this method. Firstly, the hierarchical density estimation is used to remove single and small cluster outliers. Then a progressive TIN method is used to find non-outliers removed in the previous steps. The experimental results indicate the effectiveness of this method in dealing with the two types of points cloud data. And this method can also handle low quality point cloud data from image matching. The quantitative analysis shows that the outlier detection rate is higher than 97%.

Key words： outlier detection; points cloud data; hierarchical; LiDAR; image matching

Cite this article

ZHU Junfeng , HU Xiangyun , ZHANG Zuxun , XIONG Xiaodong . Hierarchical Outlier Detection for Point Cloud Data Using a Density Analysis Method[J]. Acta Geodaetica et Cartographica Sinica, 2015 , 44(3) : 282 -291 . DOI: 10.11947/j.AGCS.2015.20130423

References

[1] AXELSSON P. DEM Generation from Laser Scanner Data Using Adaptive TIN Models[C]//Proceedings of International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Amsterdam: [s.n.], 2000: 110-117.
[2] SUI Lichun,YANG Yun. 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. (隋立春,杨耕. 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.)
[3] SUI Lichun, ZHANG Yibin, LIU Yan, et al.Filtering of Airborne LiDAR Point Cloud Data Based on Adaptive Mathematical Morphology[J]. Acta Geodaetica et Cartographica Sinica, 2010, 39(4); 390-396. (隋立春,张熠斌,柳艳, 等. 基于改进的数学形态学算法的LiDAR点云数据滤波[J]. 测绘学报, 2010, 39(4): 390-396.)
[4] ZHANG Yi, YAN Li. 3D Diffusion Filtering Method of Intensity Noise for Terrestrial Laser Scanning Point Cloud[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(4): 568-573. (张毅,闫利. 地面激光点云强度噪声的三维扩散滤波方法[J].测绘学报, 2010, 39(4); 219-224.)
[5] CHENG Liang, GONG Jianya. Building Boundary Extraction Using Very High Resolution Images and LiDAR[J]. Acta Geodaetica et Cartographica Sinica, 2008, 37(3):391-393. (程亮,龚健雅. LiDAR辅助下利用超高分辨率影像提取建筑物轮廓方法[J]. 测绘学报, 2008, 37(3): 391-393.)
[6] HASLER D, SBAIZ L, SVSSTRUNK S, et al. Outlier Modeling in Image Matching[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(3): 301-315.
[7] 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.
[8] ZUO Zhiquan, ZHANG Zuxun, ZHANG Jianqing. Noise Removal Algorithm of LIDAR Point Clouds Based on 3D Finite-element[J]. Journal of Remote Sensing, 2012, 16(2): 297-309.(左志权,张祖勋,张剑清.三维有限元分析的LIDAR点云噪声剔除算法[J].遥感学报,2012, 16(2): 297-309.)
[9] HAN Wenjun,ZUO Zhiquan. Noise Removing Algorithm of LiDAR Point Clouds Based on TIN Smoothing Rules[J]. Journal of Surveying and Mapping, 2012, 37(6): 153-154. (韩文军,左志权. 基于三角网光滑规则的LiDAR 点云噪声剔除算法[J]. 测绘科学, 2012, 37(6): 153-154.)
[10] SILVÁN-CÁRDENAS J L, WANG L. A Multi-resolution Approach for Filtering LiDAR Altimetry Data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2006, 61(1): 11-22.
[11] MENG X, WANG L, SILVÁN-CÁRDENAS J L, et al. A Multidirectional Ground Filtering Algorithm for Airborne LiDAR[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2009, 64(1): 117-124.
[12] ZHANG K Q, CHEN S, 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.
[13] MONGUS D, ZALIK B. Parameter-free Ground Filtering of LiDAR Data for Automatic DTM Generation[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 67: 1-12.
[14] KOBLER A, PFEIFER N, OGRINC P, et al. Repetitive Interpolation: A Robust Algorithm for DTM Generation from Aerial Laser Scanner Data in Forested Terrain[J]. Remote Sensing of Environment, 2007, 108(1): 9-23.
[15] SOTOODEH S. Outlier Detection in Laser Scanner Point Clouds[C]//Proceedings of the ISPRS Commission V Symposium, Image Engineering and Vision Metrology, Commission V: 36. Dresden: ISPRS, 2006: 297-302.
[16] SOTOODEH S. Hierarchical Clustered Outlier Detection in Laser Scanner Point Clouds[C]//Proceedings of ISPRS Commission V Symposium Image Enginerring and Vision Metrology. Deresden: ISPRS, 2007: 383-387.
[17] ALMEIDA J A S, BARBOSA L M S, PAIS A, et al. Improving Hierarchical Cluster Analysis: A New Method with Outlier Detection and Automatic Clustering[J]. Chemometrics and Intelligent Laboratory Systems, 2007, 87(2): 208-217.
[18] DESBRUN M, MEYER M, SCHROBDER P, et al. Implicit Fairing of Irregular Meshes Using Diffusion and Curvature Flow[C]//Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques. Los Angeles: [s.n.], 1999: 317-324.
[19] BAJAJ C L, XU G. Anisotropic Diffusion of Surfaces and Functions on Surfaces[J]. ACM Transactions on Graphics, 2003, 22(1): 4-32.
[20] CLARENZ U, DIEWALD U, RUMPF M. Anisotropic Geometric Diffusion in Surface Processing[C]//Proceedings of IEEE Visualization 2000. Salt Lake City: IEEE, 2000: 397-412.
[21] YE Aifen, GONG Shengrong, WANG Zhaohui, et al. Point Cloud Density Extraction Based on Stochastic Distribution Estimation[J]. Computer Engineering, 2009, 35(4): 183-186. (叶爱芬,龚声蓉,王朝晖,等.基于随机分布估计的点云密度提取[J]. 计算机工程, 2009, 35(4): 183-186.)
[22] PATEL J K, READ C B. Handbook of the Normal Distribution[M]. 2nd ed. London: CRC Press, 1996.
[23] MANN P S. Introductory Statistics[M]. 7th ed. New York: John Wiley and Sons Inc, 2010.
[24] PCL. Point Cloud Library(PCL) Module Filters[EB/OL]. [2013-06-12]. http://docs.pointclouds. org/trunk/group_filters.html.
[25] ISPRS. ISPRS Test on Extracting DEMS from Point Clouds: A Comparison of Existing Automatic Filters[EB/OL].2006[2013-08-06]. http://www.itc.nl/isprswgiii-3/filtertest.
Options
Outlines

/

〈 〉