Classification of Photogrammetric and Airborne LiDAR Point Clouds Using Machine Learning Algorithms

被引:16
|
作者
Duran, Zaide [1 ]
Ozcan, Kubra [1 ]
Atik, Muhammed Enes [1 ]
机构
[1] Istanbul Tech Univ, Dept Geomat Engn, TR-34469 Istanbul, Turkey
关键词
photogrammetry; LiDAR; point cloud; classification; machine learning; UAV; REGRESSION-ANALYSIS; LOW-COST;
D O I
10.3390/drones5040104
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
With the development of photogrammetry technologies, point clouds have found a wide range of use in academic and commercial areas. This situation has made it essential to extract information from point clouds. In particular, artificial intelligence applications have been used to extract information from point clouds to complex structures. Point cloud classification is also one of the leading areas where these applications are used. In this study, the classification of point clouds obtained by aerial photogrammetry and Light Detection and Ranging (LiDAR) technology belonging to the same region is performed by using machine learning. For this purpose, nine popular machine learning methods have been used. Geometric features obtained from point clouds were used for the feature spaces created for classification. Color information is also added to these in the photogrammetric point cloud. According to the LiDAR point cloud results, the highest overall accuracies were obtained as 0.96 with the Multilayer Perceptron (MLP) method. The lowest overall accuracies were obtained as 0.50 with the AdaBoost method. The method with the highest overall accuracy was achieved with the MLP (0.90) method. The lowest overall accuracy method is the GNB method with 0.25 overall accuracy.
引用
收藏
页数:16
相关论文
共 50 条
  • [11] Exploring Airborne LiDAR and Aerial Photographs Using Machine Learning for Land Cover Classification
    Tsai, Ming-Da
    Tseng, Kuan-Wen
    Lai, Chia-Cheng
    Wei, Chun-Ta
    Cheng, Ken-Fa
    [J]. REMOTE SENSING, 2023, 15 (09)
  • [12] Progressive Filtering of Airborne LiDAR Point Clouds Using Graph Cuts
    He, Yuxiang
    Zhang, Chunsun
    Fraser, Clive S.
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2018, 11 (08) : 2933 - 2944
  • [13] An Active Learning Method for DEM Extraction From Airborne LiDAR Point Clouds
    Hui, Zhenyang
    Jin, Shuanggen
    Cheng, Penggen
    Ziggah, Yao Yevenyo
    Wang, Leyang
    Wang, Yuqian
    Hu, Haiying
    Hu, Youjian
    [J]. IEEE ACCESS, 2019, 7 : 89366 - 89378
  • [14] CLASSIFICATION OF MULTISPECTRAL LIDAR POINT CLOUDS
    Ekhtari, Nima
    Glennie, Craig
    Fernandez-Diaz, Juan Carlos
    [J]. 2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2017, : 2756 - 2759
  • [15] SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas
    Zhang, Jixian
    Lin, Xiangguo
    Ning, Xiaogang
    [J]. REMOTE SENSING, 2013, 5 (08) : 3749 - 3775
  • [16] Deep learning for conifer/deciduous classification of airborne LiDAR 3D point clouds representing individual trees
    Hamraz, Hamid
    Jacobs, Nathan B.
    Contreras, Marco A.
    Clark, Chase H.
    [J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2019, 158 : 219 - 230
  • [17] Filtering of Airborne Lidar Point Clouds for Complex Cityscapes
    Jiang Jingjue
    Zhang Zuxun
    Ming Ying
    [J]. GEO-SPATIAL INFORMATION SCIENCE, 2008, 11 (01) : 21 - 25
  • [18] Positioning errors analysis on airborne LIDAR point clouds
    Li, Feng
    Cui, Ximin
    Liu, Xiaoyang
    Wei, Aixia
    Wu, Yanxiong
    [J]. Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering, 2014, 43 (06): : 1842 - 1849
  • [19] Object Segmentation of Cluttered Airborne LiDAR Point Clouds
    Caros, Mariona
    Just, Ariadna
    Segui, Santi
    Vitria, Jordi
    [J]. ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT, 2022, 356 : 259 - 268
  • [20] Classification of airborne LiDAR point cloud data based on information vector machine
    Liu, Zhi-Qing
    Li, Peng-Cheng
    Chen, Xiao-Wei
    Zhang, Bao-Ming
    Guo, Hai-Tao
    [J]. Guangxue Jingmi Gongcheng/Optics and Precision Engineering, 2016, 24 (01): : 210 - 219