Hierarchical Threshold Adaptive for Point Cloud Filter Algorithm of Moving Surface Fitting

doi:10.11947/j.AGCS.2018.20170491

Abstract

Abstract: In order to improve the accuracy,efficiency and adaptability of point cloud filtering algorithm,a hierarchical threshold adaptive for point cloud filter algorithm of moving surface fitting was proposed.Firstly,the noisy points are removed by using a statistic histogram method.Secondly,the grid index is established by grid segmentation,and the surface equation is set up through the lowest point among the neighborhood grids.The real height and fit are calculated.The difference between the elevation and the threshold can be determined.Finally,in order to improve the filtering accuracy,hierarchical filtering is used to change the grid size and automatically set the neighborhood size and threshold until the filtering result reaches the accuracy requirement.The test data provided by the International Photogrammetry and Remote Sensing Society (ISPRS) is used to verify the algorithm.The first and second error and the total error are 7.33%,10.64% and 6.34% respectively.The algorithm is compared with the eight classical filtering algorithms published by ISPRS.The experiment results show that the method has well-adapted and it has high accurate filtering result.

Key words: point cloud data, gridding, moving curve surface, neighborhood window, hierarchical filtering, surface fitting

CLC Number:

P237

ZHU Xiaoxiao, WANG Cheng, XI Xiaohuan, WANG Pu, TIAN Xinguang, YANG Xuebo. Hierarchical Threshold Adaptive for Point Cloud Filter Algorithm of Moving Surface Fitting[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(2): 153-160.

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