The urban objects classification based on LiDAR 3D

被引:0
|
作者
Huang, Zuowei [1 ,2 ]
He, Shu [1 ]
Qiu, Luo [2 ]
机构
[1] Hunan Univ Technol, Sch Architecture & Urban Planning, Zhuzhou 412008, Peoples R China
[2] Cent S Univ, Sch Geosci & Informat Phys, Changsha 410083, Peoples R China
关键词
LIDAR; point cloud; 3D digital Image; Objects classification; ANN;
D O I
10.4028/www.scientific.net/AMM.226-228.1840
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
The Lidar technology can quickly acquire three-dimensional information of earth surface with high precision. As a new technique of data collection acquisition,it has been gradually applied to many industry. The classification on point cloud is the premise and key step to the feature extraction and the model building of objects. Based on LiDAR 3D and ANN This paper presents a classification method mainly suitable for the urban's typical objects. It takes the changsha city as the study area,to verify the effectiveness of this method. The result shows that,this classification method successfully classifies the categories of the ground,buildings,trees and bare lands.
引用
收藏
页码:1840 / +
页数:2
相关论文
共 50 条
  • [1] Online algorithms for classification of urban objects in 3D point clouds
    Stamos, Ioannis
    Hadjiliadis, Olympia
    Zhang, Hongzhong
    Flynn, Thomas
    [J]. SECOND JOINT 3DIM/3DPVT CONFERENCE: 3D IMAGING, MODELING, PROCESSING, VISUALIZATION & TRANSMISSION (3DIMPVT 2012), 2012, : 332 - 339
  • [2] A New Geometric 3D LiDAR Feature for Model Creation and Classification of Moving Objects
    Kusenbach, Michael
    Himmelsbach, Michael
    Wuensche, Hans-Joachim
    [J]. 2016 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV), 2016, : 272 - 278
  • [3] A New Approach to 3D Dense LiDAR Data Classification in Urban Environment
    Inshu Chauhan
    Claus Brenner
    R. D. Garg
    M. Parida
    [J]. Journal of the Indian Society of Remote Sensing, 2014, 42 : 673 - 678
  • [4] A New Approach to 3D Dense LiDAR Data Classification in Urban Environment
    Chauhan, Inshu
    Brenner, Claus
    Garg, R. D.
    Parida, M.
    [J]. JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING, 2014, 42 (03) : 673 - 678
  • [5] Intelligent 3D Objects Classification for Vehicular Ad Hoc Network Based on Lidar and Deep Learning Approaches
    de Sousa, Pedro Henrique Feijo
    Almeida, Jefferson Silva
    Ohata, Elene Firmeza
    Nogueira, Fabricio Gonzalez
    Torrico, Bismark Claure
    de Albuquerque, Victor Hugo Costa
    Hassan, Mohammad Mehedi
    Kumar, Neeraj
    Hassan, Md. Rafiul
    Reboucas Filho, Pedro Pedrosa
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (10) : 19807 - 19816
  • [6] Improvement of 3D LiDAR point cloud classification of urban road environment based on random forest classifier
    Mohamed, Mahmoud
    Morsy, Salem
    El-Shazly, Adel
    [J]. GEOCARTO INTERNATIONAL, 2022, 37 (27) : 15604 - 15626
  • [7] Mapless Online Detection of Dynamic Objects in 3D Lidar
    Yoon, David J.
    Tang, Tim Y.
    Barfoot, Timothy D.
    [J]. 2019 16TH CONFERENCE ON COMPUTER AND ROBOT VISION (CRV 2019), 2019, : 113 - 120
  • [8] AUTOMATIC CLASSIFICATION AND 3D MODELING OF LIDAR DATA
    Moussa, A.
    El-Sheimy, N.
    [J]. PCV 2010: PHOTOGRAMMETRIC COMPUTER VISION AND IMAGE ANALYSIS, PT II, 2010, 38 : 155 - 159
  • [9] An Interactive Visual Analytic Tool for Semantic Classification of 3D Urban LiDAR Point Cloud
    Kumari, Beena
    Sreevalsan-Nair, Jaya
    [J]. 23RD ACM SIGSPATIAL INTERNATIONAL CONFERENCE ON ADVANCES IN GEOGRAPHIC INFORMATION SYSTEMS (ACM SIGSPATIAL GIS 2015), 2015,
  • [10] Deep Semantic Classification for 3D LiDAR Data
    Dewan, Ayush
    Oliveira, Gabriel L.
    Burgard, Wolfram
    [J]. 2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2017, : 3544 - 3549