Detection, segmentation and localization of individual trees from mms point cloud data

In this paper, we address the extraction of objects from 3D point clouds acquired with mobile mapping systems. More specifically, we focus on the detection of tree-like objects, a subsequent segmentation of individual trees and a localization of the respective trees. Thereby, the detection of tree-like objects is achieved via a binary point-wise classification based on geometric features, which categorizes each point of the 3D point cloud into either tree-like objects or non-tree-like objects. The subsequent segmentation and localization of individual trees is carried out by applying a 2D projection and a mean shift segmentation on a downsampled version of that part of the original 3D point cloud which represents all tree-like objects, and it also involves a segment-based shape analysis to only retain plausible tree segments. We demonstrate the performance of our framework on a benchmark dataset which contains 10:13M 3D points and has been acquired with a mobile mapping system in the city of Delft in the Netherlands.

[1]  Bruno Vallet,et al.  3D OCTREE BASED WATERTIGHT MESH GENERATION FROM UBIQUITOUS DATA , 2015 .

[2]  Roderik Lindenbergh,et al.  Automatic classification of trees from laser scanning point clouds , 2015 .

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

[4]  Frédéric Bretar,et al.  3-D mapping of a multi-layered Mediterranean forest using ALS data , 2012 .

[5]  Xiao Xiang Zhu,et al.  SEGMENTATION AND CROWN PARAMETER EXTRACTION OF INDIVIDUAL TREES IN AN AIRBORNE TOMOSAR POINT CLOUD , 2015 .

[6]  Wei Yao,et al.  Automated detection of 3 D individual trees along urban road corridors by mobile laser scanning systems , 2013 .

[7]  Marco Heurich,et al.  ENHANCED DETECTION OF 3D INDIVIDUAL TREES IN FORESTED AREAS USING AIRBORNE FULL-WAVEFORM LIDAR DATA BY COMBINING NORMALIZED CUTS WITH SPATIAL DENSITY CLUSTERING , 2013 .

[8]  S. J. Oude Elberink,et al.  User-assisted Object Detection by Segment Based Similarity Measures in Mobile Laser Scanner Data , 2014 .

[9]  Xiao Xiang Zhu,et al.  Reconstruction of Individual Trees from Multi-Aspect TomoSAR Data , 2015 .

[10]  Mandy Eberhart,et al.  Decision Forests For Computer Vision And Medical Image Analysis , 2016 .

[11]  Martial Hebert,et al.  Contextual classification with functional Max-Margin Markov Networks , 2009, CVPR.

[12]  Markus H. Gross,et al.  Multi‐scale Feature Extraction on Point‐Sampled Surfaces , 2003, Comput. Graph. Forum.

[13]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

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

[15]  S. J. Oude Elberink,et al.  IQPC 2015 TRACK: TREE SEPARATION AND CLASSIFICATION IN MOBILE MAPPING LIDAR DATA , 2015 .

[16]  J. Reitberger,et al.  3D segmentation of single trees exploiting full waveform LIDAR data , 2009 .

[17]  Chao Chen,et al.  Using Random Forest to Learn Imbalanced Data , 2004 .

[18]  MARTIN WEINMANN,et al.  Segmentation and Localization of Individual Trees from MMS Point Cloud Data Acquired in Urban Areas , 2016 .

[19]  Kun Liu,et al.  The Iqmulus Urban Showcase: Automatic Tree Classification and Identification in Huge Mobile Mapping Point Clouds , 2016 .

[20]  Yizong Cheng,et al.  Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Bruno Vallet,et al.  TREES DETECTION FROM LASER POINT CLOUDS ACQUIRED IN DENSE URBAN AREAS BY A MOBILE MAPPING SYSTEM , 2012 .

[22]  Larry D. Hostetler,et al.  The estimation of the gradient of a density function, with applications in pattern recognition , 1975, IEEE Trans. Inf. Theory.

[23]  Sandeep Gupta,et al.  Comparative Analysis of Clustering-Based Approaches for 3-D Single Tree Detection Using Airborne Fullwave Lidar Data , 2010, Remote. Sens..

[24]  Martial Hebert,et al.  3-D scene analysis via sequenced predictions over points and regions , 2011, 2011 IEEE International Conference on Robotics and Automation.

[25]  Thomas Melzer,et al.  Non-parametric segmentation of ALS point clouds using mean shift , 2007 .

[26]  James R. Lersch,et al.  Context-driven automated target detection in 3D data , 2004, SPIE Defense + Commercial Sensing.

[27]  Martial Hebert,et al.  Scale selection for classification of point-sampled 3D surfaces , 2005, Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM'05).

[28]  Dimitri Lague,et al.  3D Terrestrial LiDAR data classification of complex natural scenes using a multi-scale dimensionality criterion: applications in geomorphology , 2011, ArXiv.

[29]  Bruno Vallet,et al.  TerraMobilita/IQmulus Urban Point Cloud Classification Benchmark , 2014 .

[30]  Roderik Lindenbergh,et al.  Automated large scale parameter extraction of road-side trees sampled by a laser mobile mapping system , 2015 .

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

[32]  Martial Hebert,et al.  Efficient 3-D scene analysis from streaming data , 2013, 2013 IEEE International Conference on Robotics and Automation.

[33]  Martial Hebert,et al.  Onboard contextual classification of 3-D point clouds with learned high-order Markov Random Fields , 2009, 2009 IEEE International Conference on Robotics and Automation.

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

[35]  Uwe Stilla,et al.  Potential of airborne single-pass millimeterwave InSAR data for individual tree recognition , 2013 .

[36]  Martin Weinmann,et al.  Book Review–Reconstruction and Analysis of 3D Scenes: From Irregularly Distributed 3D Points to Object Classes , 2016, Photogrammetric Engineering & Remote Sensing.

[37]  François Goulette,et al.  Paris-rue-Madame Database - A 3D Mobile Laser Scanner Dataset for Benchmarking Urban Detection, Segmentation and Classification Methods , 2014, ICPRAM.

[38]  J. Demantké,et al.  DIMENSIONALITY BASED SCALE SELECTION IN 3D LIDAR POINT CLOUDS , 2012 .

[39]  Konrad Schindler,et al.  FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY VARYING DENSITY , 2016 .

[40]  G. Vosselman Point cloud segmentation for urban scene classification , 2013 .

[41]  Martial Hebert,et al.  Directional Associative Markov Network for 3-D Point Cloud Classification , 2008 .