Bulletin of Surveying and Mapping ›› 2021, Vol. 0 ›› Issue (10): 67-72,131.doi: 10.13474/j.cnki.11-2246.2021.307

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Block filtering method for LiDAR point cloud fusion image

MAO Donghai1,2, LI Shoujun1,2,3, WANG Feng4, DAI Huayi1, YANG Fanlin1,5   

  1. 1. College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China;
    2. Key Laboratory of Submarine Geosciences, Ministry of Natural Resources, Hangzhou 310012, China;
    3. Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China;
    4. Zhejiang Engineering Geophysical Prospecting and Design Institute Co., Ltd., Hangzhou 310005, China;
    5. Key Laboratory of Marine Surveying and Mapping, Ministry of Natural Resources, Qingdao 266590, China
  • Received:2021-02-01 Revised:2021-08-14 Online:2021-10-25 Published:2021-11-13

Abstract: Aiming at the problem that the existing LiDAR ground point filtering algorithm is not adaptable to complex terrain and objects, a block filtering method combining point cloud and ground image is proposed in this paper. Firstly, the ground image is matched with the point cloud to make it obtain more spectral texture information from the image. Secondly, the ground feature spectrum, forest land relative density, point cloud elevation characteristics, DSM model and its slope are fully analyzed, and the original point cloud is cut into several independent blocks based on decision-making level fusion. Finally, according to the different multivariate detail characteristics of each region, the IPTD filtering algorithm is improved and the parameters are optimized by the search method to obtain the optimal and robust results. Using the filtered total ground points, the DEM model obtained by interpolation algorithm and related experiments verify the superiority of the proposed algorithm.

Key words: data fusion, LiDAR point cloud, segmentation filtering, multivariate characteristics, DEM

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