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An Improved DBSCAN Method for LiDAR Data Segmentation with Automatic Eps Estimation 원문보기

Sensors, v.19 no.1, 2019년, pp.172 -   

Wang, Chunxiao (Geomatics College, Shandong University of Science and Technology, Qingdao 266590, China) ,  Ji, Min (cx8989@163.com (C.W.)) ,  Wang, Jian (rainbowwj@126.com (J.W.)) ,  Wen, Wei (liting_sdust@126.com (T.L.)) ,  Li, Ting (ttsunyong@163.com (Y.S.)) ,  Sun, Yong (Geomatics College, Shandong University of Science and Technology, Qingdao 266590, China)

Abstract AI-Helper 아이콘AI-Helper

Point cloud data segmentation, filtering, classification, and feature extraction are the main focus of point cloud data processing. DBSCAN (density-based spatial clustering of applications with noise) is capable of detecting arbitrary shapes of clusters in spaces of any dimension, and this method is...

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참고문헌 (59)

  1. 1. Akel N.A. Kremeike K. Filin S. Sester M. Doytsher Y. Dense DTM generalization aided by roads extracted from LiDAR data Proceedings of the ISPRS WG III/3, III/4, V/3 Workshop “Laser scanning 2005” Enschede, The Netherlands 12–14 September 2005 54 59 

  2. 2. Popescu S.C. Wynne R.H. Seeing the trees in the forest: Using lidar and multispectral data fusion with local filtering and variable window size for estimating tree height Photogramm. Eng. Remote Sens. 2004 70 589 604 10.14358/PERS.70.5.589 

  3. 3. Bortolot Z.J. Wynne R.H. Estimating forest biomass using small footprint LiDAR data: An individual tree-based approach that incorporates training data ISPRS J. Photogramm. Remote Sens. 2005 59 342 360 10.1016/j.isprsjprs.2005.07.001 

  4. 4. Hollaus M. Wagner W. Eberhöfer C. Karel W. Accuracy of large-scale canopy heights derived from LiDAR data under operational constraints in a complex alpine environment ISPRS J. Photogramm. Remote Sens. 2006 60 323 338 10.1016/j.isprsjprs.2006.05.002 

  5. 5. Garcia-Alonso M. Ferraz A. Saatchi S.S. Casas A. Koltunov A. Ustin S. Ramirez C. Balzter H. Estimating forest biomass from LiDAR data: A comparison of the raster-based and point-cloud data approach Proceedings of the AGU Fall Meeting San Francisco, CA, USA 14–18 December 2015 

  6. 6. Murakami H. Nakagawa K. Hasegawa H. Shibata T. Iwanami E. Change detection of buildings using an airborne laser scanner ISPRS J. Photogramm. Remote Sens. 1999 54 148 152 10.1016/S0924-2716(99)00006-4 

  7. 7. Gomes Pereira L. Janssen L. Suitability of laser data for DTM generation: A case study in the context of road planning and design ISPRS J. Photogramm. Remote Sens. 1999 54 244 253 10.1016/S0924-2716(99)00018-0 

  8. 8. Clode S. Rottensteiner F. Kootsookos P. Zelniker E. Detection and vectorisation of roads from lidar data Photogramm. Eng. Remote Sens. 2006 73 517 535 10.14358/PERS.73.5.517 

  9. 9. Quattoni A. Torralba A. Recognizing indoor scenes Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition Miami, FL, USA 20–25 June 2009 

  10. 10. Inokuchi H. Multi-Lidar System U.S. Patent Application No. 20120092645A1 19 4 2012 

  11. 11. Ester M. Kriegel H.P. Sander J. Xu X. A density-based algorithm for discovering clusters in large spatial databases with noise Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland, OR, USA 2–4 August 1996 

  12. 12. Ankerst M. Breunig M.M. Kriegel H.P. Sander J. OPTICS: Ordering points to identify the clustering structure Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data Philadelphia, PA, USA 31 May–3 June 1999 49 60 

  13. 13. Hinneburg A. Gabriel H.-H. DENCLUE 2.0: Fast Clustering Based on Kernel Density Estimation Intelligent Data Analysis VII, Proceedings of the 7th International Symposium on Intelligent Data Analysis, IDA 2007, Ljubljana, Slovenia, 6–8 September 2007 Berthold M.R. Shawe-Taylor J. Lavrač N. Springer Berlin, Germany 2007 70 80 

  14. 14. Han J. Kamber M. Density-Based Methods Data Mining: Concepts and Technique Morgan Kaufmann Publishers Burlington, MA, USA 2006 Chapter 7 418 422 

  15. 15. Ghosh S. Lohani B. Heuristical Feature Extraction from LIDAR Data and Their Visualization ISPRS—Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2012 38 13 18 10.5194/isprsarchives-XXXVIII-5-W12-13-2011 

  16. 16. Schubert E. Sander J. Ester M. Kriegel H.P. Xu X. DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN ACM Trans. Database Syst. 2017 42 1 21 10.1145/3068335 

  17. 17. Sander J. Ester M. Kriegel H.P. Xu X. Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications Data Min. Knowl. Discov. 1998 2 169 194 10.1023/A:1009745219419 

  18. 18. Daszykowski M. Walczak B. Massart D.L. Looking for natural patterns in data: Part 1. Density-based approach Chemom. Intell. Lab. Syst. 2001 56 83 92 10.1016/S0169-7439(01)00111-3 

  19. 19. Dua D. Karra Taniskidou E. UCI Machine Learning Repository. University of California, School of Information and Computer Science: Irvine, CA, USA 2017 Available online: http://archive.ics.uci.edu/ml (accessed on 3 January 2019) 

  20. 20. Gan J. Tao Y. DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data Melbourne, Australia 31 May–4 June 2015 519 530 

  21. 21. Hubert L. Arabie P. Comparing partitions J. Classif. 1985 2 193 218 10.1007/BF01908075 

  22. 22. Ghosh S. Lohani B. Mining lidar data with spatial clustering algorithms Int. J. Remote Sens. 2013 34 5119 5135 10.1080/01431161.2013.787499 

  23. 23. Lari Z. Habib A. Alternative methodologies for the estimation of local point density index: Moving towards adaptive LiDAR data processing Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2012 39 127 132 10.5194/isprsarchives-XXXIX-B3-127-2012 

  24. 24. Biosca J.M. Lerma J.L. Unsupervised robust planar segmentation of terrestrial laser scanner point clouds based on fuzzy clustering methods ISPRS J. Photogramm. Remote. Sens. 2008 63 84 98 10.1016/j.isprsjprs.2007.07.010 

  25. 25. Filin S. Surface clustering from airborne laser scanning data Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2002 34 119 124 

  26. 26. Jiang B. Extraction of Spatial Objects from Laser-Scanning data using a clustering technique Proceedings of the XXth ISPRS Congress Istanbul, Turkey 12–13 July 2004 

  27. 27. Morsdorf F. Meier E. Allgöwer B. Nüesch D. Clustering in airborne laser scanning raw data for segmentation of single trees Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2003 34 W13 

  28. 28. Roggero M. Object segmentation with region growing and principal component analysis Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2002 34 289 294 

  29. 29. Crosilla F. Visintini D. Sepic F. A statistically proven automatic curvature based classification procedure of laser points Proceedings of the XXI ISPRS Congress Beijing, China 3–11 July 2008 

  30. 30. Jain A.K. Duin R.P.W. Mao J. Statistical pattern recognition: A review IEEE Trans. Pattern Anal. Mach. Intell. 2000 22 4 37 10.1109/34.824819 

  31. 31. Ballard D.H. Generalizing the Hough transform to detect arbitrary shapes Pattern Recognit. 1981 13 111 122 10.1016/0031-3203(81)90009-1 

  32. 32. Tarsha-Kurdi F. Tania L. Pierre G. Hough-transform and extended ransac algorithms for automatic detection of 3d building roof planes from lidar data Int. Arch. Photogramm. Remote Sens. Spat. Inf. Syst. 2007 36 407 412 

  33. 33. Fischler M. Bolles R. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography Commun. ACM 1981 24 381 395 10.1145/358669.358692 

  34. 34. Hoffman R. Jain A.K. Segmentation and classification of range images IEEE Trans. Pattern Anal. Mach. Intell. 1987 5 608 620 10.1109/TPAMI.1987.4767955 

  35. 35. Yang B. Huang R. Dong Z. Zang Y. Li J. Two-step adaptive extraction method for ground points and breaklines from lidar point clouds ISPRS J. Photogramm. Remote Sens. 2016 119 373 389 10.1016/j.isprsjprs.2016.07.002 

  36. 36. Maas H.G. Vosselman G. Two algorithms for extracting building models from raw laser altimetry data ISPRS J. Photogramm. Remote. Sens. 1999 54 153 163 10.1016/S0924-2716(99)00004-0 

  37. 37. Riveiro B. González-Jorge H. Martínez-Sánchez J. Díaz-Vilariño L. Arias P. Automatic detection of zebra crossings from mobile LiDAR data Opt. Laser Technol. 2015 70 63 70 10.1016/j.optlastec.2015.01.011 

  38. 38. Neidhart H. Sester M. Extraction of building ground plans from Lidar data Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2008 37 405 410 

  39. 39. Woo H. Kang E. Wang S. Lee K.H. A new segmentation method for point cloud data Int. J. Mach. Tools Manuf. 2002 42 167 178 10.1016/S0890-6955(01)00120-1 

  40. 40. Su Y.-T. Bethel J. Hu S. Octree-based segmentation for terrestrial LiDAR point cloud data in industrial applications ISPRS J. Photogramm. Remote Sens. 2016 113 59 74 10.1016/j.isprsjprs.2016.01.001 

  41. 41. Vo A.V. Truong-Hong L. Laefer D.F. Bertolotto M. Octree-based region growing for point cloud segmentation ISPRS J. Photogramm. Remote. Sens. 2015 104 88 100 10.1016/j.isprsjprs.2015.01.011 

  42. 42. Boulaassal H. Landes T. Grussenmeyer P. Tarsha-Kurdi F. Automatic segmentation of building facades using terrestrial laser data Proceedings of the ISPRS Workshop on Laser Scanning 2007 and SilviLaser Espoo, Finland 12–14 September 2007 Volume XXXVI 65 70 

  43. 43. Schnabel R. Wahl R. Klein R. Efficient RANSAC for Point-Cloud Shape Detection Computer Graphics Forum Wiley Online Library Hoboken, NJ, USA 2007 

  44. 44. Awwad T.M. Zhu Q. Du Z. Zhang Y. An improved segmentation approach for planar surfaces from unstructured 3D point clouds Photogramm. Rec. 2010 25 5 23 10.1111/j.1477-9730.2009.00564.x 

  45. 45. Schwalbe E. Maas H.-G. Seidel F. 3D building model generation from airborne laser scanner data using 2D GIS data and orthogonal point cloud projections Proceedings of the ISPRS WG III/3, III/4 Enschede, The Netherlands 12–14 September 2005 Volume 3 12 14 

  46. 46. Moosmann F. Pink O. Stiller C. Segmentation of 3D lidar data in non-flat urban environments using a local convexity criterion Proceedings of the 2009 IEEE Intelligent Vehicles Symposium Xi’an, China 3–5 June 2009 

  47. 47. Douillard B. Douillard B. Underwood J. Kuntz N. Vlaskine V. Quadros A. Morton P. Frenkel A. On the segmentation of 3D LIDAR point clouds Proceedings of the 2011 IEEE International Conference on Robotics and Automation Shanghai, China 9–13 May 2011 

  48. 48. Besl P.J. Jain R.C. Segmentation through variable-order surface fitting IEEE Trans. Pattern Anal. Mach. Intell. 1988 10 167 192 10.1109/34.3881 

  49. 49. Rabbani T. Automatic Reconstruction of Industrial Installations Using Point Clouds and Images NCG Delft, The Netherlands 2006 

  50. 50. Hofmann A. Analysis of TIN-structure parameter spaces in airborne laser scanner data for 3-D building model generation Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2004 35 302 307 

  51. 51. Wang C.-K. Hsu P.-H. Building Extraction from LiDAR Data Using Wavelet Analysis Proceedings of the 27th Asian Conference on Remote Sensing Ulaanbaatar, Mongolia 9–13 October 2006 

  52. 52. Höfle B. Hollaus M. Hagenauer J. Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data ISPRS J. Photogramm. Remote Sens. 2012 67 134 147 10.1016/j.isprsjprs.2011.12.003 

  53. 53. Niemeyer J. Rottensteiner F. Soergel U. Contextual classification of lidar data and building object detection in urban areas ISPRS J. Photogramm. Remote Sens. 2014 87 152 165 10.1016/j.isprsjprs.2013.11.001 

  54. 54. Anguelov D. Taskarf B. Chatalbashev V. Koller D. Gupta D. Heitz G. Ng A. Discriminative learning of markov random fields for segmentation of 3d scan data Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition San Diego, CA, USA 20–25 June 2005 

  55. 55. Triebel R. Kersting K. Burgard W. Robust 3D scan point classification using associative Markov networks Proceedings of the IEEE International Conference on Robotics and Automation Orlando, FL, USA 15–19 May 2006 

  56. 56. Meng X. Currit N. Zhao K. Ground Filtering Algorithms for Airborne LiDAR Data: A Review of Critical Issues Remote Sens. 2010 2 833 860 10.3390/rs2030833 

  57. 57. Rusu R.B. Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments KI-Künstliche Intell. 2010 24 345 348 10.1007/s13218-010-0059-6 

  58. 58. Campello R.J.G.B. Moulavi D. Sander J. Density-Based Clustering Based on Hierarchical Density Estimates Lecture Notes in Computer Science Pei J. Tseng V.S. Cao L. Motoda H. Xu G. Advances in Knowledge Discovery and Data Mining Springer Berlin, Germany 2013 Volume 7819 

  59. 59. Hoover A. Jean-Baptiste G. Jiang X. Flynn P.J. Bunke H. Goldgof D.B. Fisher R.B. An experimental comparison of range image segmentation algorithms IEEE Trans. Pattern Anal. Mach. Intell. 1996 18 673 689 10.1109/34.506791 

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