A probabilistic graphical model for the classification of mobile LiDAR point clouds

Abstract Mobile Light Detection And Ranging (LiDAR) point clouds have the characteristics of complex and incomplete scenes, uneven point density and noises, which raises great challenges for automatically interpreting 3D scene. Aiming at the problem of 3D point cloud classification, we propose a probabilistic graphical model for automatic classification of mobile LiDAR point clouds in this paper. First, the super-voxels are generated as primitives based on the similar geometric and radiometric properties. Second, we construct point-based multi-scale visual features that are used to describe the texture information at various scales. Third, the topic model is used to analyze the semantic correlations among points within super-voxels to establish the semantic representation, which is finally fed into the proposed probabilistic graphical model. The proposed model combines Bayesian network and Markov random fields to obtain locally continuous and globally optimal classification results. To evaluate the effectiveness and the robustness of the proposed method, experiments were conducted using mobile LiDAR point clouds for three types of street scenes. Experimental results demonstrate that our proposed model is efficient and robust for extracting vehicles, buildings, street trees and pole-like objects, with overall accuracies of 98.17%, 97.41% and 96.81% respectively. Moreover, compared with other existing methods, our proposed model can provide higher classification correctness, specifically for small objects such as cars and pole-like objects.

[1]  Yunhao Chen,et al.  An Object-Oriented Semantic Clustering Algorithm for High-Resolution Remote Sensing Images Using the Aspect Model , 2011, IEEE Geoscience and Remote Sensing Letters.

[2]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[3]  Xiangguo Lin,et al.  Classification of ALS Point Cloud with Improved Point Cloud Segmentation and Random Forests , 2017, Remote. Sens..

[4]  F. Rottensteiner,et al.  Classification of urban LiDAR data using conditional random field and random forests , 2013, Joint Urban Remote Sensing Event 2013.

[5]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Na Li,et al.  An effective approach for land-cover classification from airborne lidar fused with co-registered data , 2012 .

[7]  Hao Liu,et al.  A hierarchical methodology for urban facade parsing from TLS point clouds , 2017 .

[8]  C. Mallet,et al.  AIRBORNE LIDAR FEATURE SELECTION FOR URBAN CLASSIFICATION USING RANDOM FORESTS , 2009 .

[9]  Jouko Lampinen,et al.  Object Classification and Recognition From Mobile Laser Scanning Point Clouds in a Road Environment , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Bruce A. Barton,et al.  Entropy and MDL discretization of continuous variables for Bayesian belief networks , 2000, Int. J. Intell. Syst..

[11]  David Suter,et al.  3D terrestrial LIDAR classifications with super-voxels and multi-scale Conditional Random Fields , 2009, Comput. Aided Des..

[12]  Zhen Wang,et al.  A Multilevel Point-Cluster-Based Discriminative Feature for ALS Point Cloud Classification , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[13]  M. Verstraete,et al.  Estimating leaf area distribution in savanna trees from terrestrial LiDAR measurements , 2011 .

[14]  V. Cerný.  Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm , 1985 .

[15]  Ahmad Kamal Aijazi,et al.  Segmentation Based Classification of 3D Urban Point Clouds: A Super-Voxel Based Approach with Evaluation , 2013, Remote. Sens..

[16]  Ruofei Zhong,et al.  Classification of Urban Point Clouds: A Robust Supervised Approach With Automatically Generating Training Data , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[17]  J. Besag.  On the Statistical Analysis of Dirty Pictures , 1986 .

[18]  Bisheng Yang,et al.  Computing multiple aggregation levels and contextual features for road facilities recognition using mobile laser scanning data , 2017 .

[19]  Martial Hebert,et al.  Parts-based 3D object classification , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[20]  Jian Yao,et al.  Segmentation-based classification for 3D urban point clouds , 2016, 2016 IEEE International Conference on Information and Automation (ICIA).

[21]  Hanan Samet,et al.  Foundations of multidimensional and metric data structures , 2006, Morgan Kaufmann series in data management systems.

[22]  Qi Zhang,et al.  Deep learning-based tree classification using mobile LiDAR data , 2015 .

[23]  Lixin Fan,et al.  Automated super-voxel based features classification of urban environments by integrating 3D point cloud and image content , 2015, 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA).

[24]  Siyang Chen,et al.  Automatic building extraction from LiDAR data fusion of point and grid-based features , 2017 .

[25]  Andrew E. Johnson,et al.  Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Deren Li,et al.  Object Classification of Aerial Images With Bag-of-Visual Words , 2010, IEEE Geoscience and Remote Sensing Letters.

[27]  Patrice Y. Simard,et al.  Best practices for convolutional neural networks applied to visual document analysis , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[28]  Gabriele Peters,et al.  Evaluation of Local 3-D Point Cloud Descriptors in Terms of Suitability for Object Classification , 2016, ICINCO.

[29]  Jing Huang,et al.  Point cloud labeling using 3D Convolutional Neural Network , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[30]  J. Niemeyer,et al.  Contextual classification of lidar data and building object detection in urban areas , 2014 .

[31]  Roderik Lindenbergh,et al.  SigVox – A 3D feature matching algorithm for automatic street object recognition in mobile laser scanning point clouds , 2017 .

[32]  Olga Veksler,et al.  Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[33]  Boris Jutzi,et al.  Feature relevance assessment for the semantic interpretation of 3D point cloud data , 2013 .

[34]  Peng Li,et al.  3-D Point Cloud Object Detection Based on Supervoxel Neighborhood With Hough Forest Framework , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[35]  Bo Guo,et al.  Discriminative-Dictionary-Learning-Based Multilevel Point-Cluster Features for ALS Point-Cloud Classification , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[36]  Konrad Schindler,et al.  Joint classification and contour extraction of large 3D point clouds , 2017 .

[37]  Steffen Urban,et al.  Distinctive 2D and 3D features for automated large-scale scene analysis in urban areas , 2015, Comput. Graph..

[38]  Juha Hyyppä,et al.  Object-based analysis of multispectral airborne laser scanner data for land cover classification and map updating , 2017 .

[39]  Rama Rao Nidamanuri,et al.  A supervoxel-based spectro-spatial approach for 3D urban point cloud labelling , 2016 .

[40]  Trevor Darrell,et al.  The pyramid match kernel: discriminative classification with sets of image features , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[41]  Stefan Hinz,et al.  Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers , 2015 .

[42]  Qing Zhu,et al.  Robust point cloud classification based on multi-level semantic relationships for urban scenes , 2017 .

[43]  Norbert Haala,et al.  Mobile LiDAR mapping for urban data capture , 2008 .

[44]  S. J. Oude Elberink,et al.  Multiple-entity based classification of airborne laser scanning data in urban areas , 2014 .

[45]  Bisheng Yang,et al.  Hierarchical extraction of urban objects from mobile laser scanning data , 2015 .

[46]  Ruofei Zhong,et al.  A Bayesian-Network-Based Classification Method Integrating Airborne LiDAR Data With Optical Images , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[47]  Boris Jutzi,et al.  CLASSIFICATION OF AIRBORNE LASER SCANNING DATA USING GEOMETRIC MULTI-SCALE FEATURES AND DIFFERENT NEIGHBOURHOOD TYPES , 2016 .

[48]  R. Zabih,et al.  Efficient Graph-Based Energy Minimization Methods in Computer Vision , 1999 .

[49]  Fan Zhang,et al.  Classification of airborne laser scanning data using JointBoost , 2015 .

[50]  Stefan Hinz,et al.  Analytical and numerical investigations on the accuracy and robustness of geometric features extracted from 3D point cloud data , 2017 .

[51]  Chenglu Wen,et al.  Patch-Based Semantic Labeling of Road Scene Using Colorized Mobile LiDAR Point Clouds , 2016, IEEE Transactions on Intelligent Transportation Systems.

[52]  Bisheng Yang,et al.  A novel binary shape context for 3D local surface description , 2017 .

[53]  Ahmad Kamal Aijazi,et al.  Automatic Detection and Parameter Estimation of Trees for Forest Inventory Applications Using 3D Terrestrial LiDAR , 2017, Remote. Sens..

[54]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[55]  Bo Du,et al.  A Three-Step Approach for TLS Point Cloud Classification , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[56]  Mihai Datcu,et al.  Semantic Annotation of Satellite Images Using Latent Dirichlet Allocation , 2010, IEEE Geoscience and Remote Sensing Letters.

[57]  A. J. Jerri.  The Shannon sampling theorem—Its various extensions and applications: A tutorial review , 1977, Proceedings of the IEEE.

[58]  Zhen Wang,et al.  A Multiscale and Hierarchical Feature Extraction Method for Terrestrial Laser Scanning Point Cloud Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[59]  Michael A. Lefsky,et al.  Volume estimates of trees with complex architecture from terrestrial laser scanning , 2008 .

[60]  S. Dreyfus,et al.  Thermodynamical Approach to the Traveling Salesman Problem : An Efficient Simulation Algorithm , 2004 .

[61]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[62]  A. J. Jerri.  Correction to "The Shannon sampling theorem—Its various extensions and applications: A tutorial review" , 1979 .