предыдущий|следующий

Point-based multi-scale morphological reconstruction filter

  • role： First author第一作者
  • Affiliation：

    College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

    Key Laboratory of Geomatics and Digital Technology of Shandong Province, Qingdao 266590,China

  • Email：bingtao_chang@163.com
  • Introduction：常兵涛，研究方向为机载LiDAR点云数据处理。E-mail： bingtao_chang@163.com
CHANG Bingtao,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

    Key Laboratory of Geomatics and Digital Technology of Shandong Province, Qingdao 266590,China

  • Email：chencf@sdust.edu.cn
  • Introduction：陈传法，研究方向为数字地形建模、点云智能处理等。E-mail：chencf@sdust.edu.cn
CHEN Chuanfa*,  
  • Affiliation：

    College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

    Key Laboratory of Geomatics and Digital Technology of Shandong Province, Qingdao 266590,China

GUO Jiaojiao,  
  • Affiliation：

    College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

    Key Laboratory of Geomatics and Digital Technology of Shandong Province, Qingdao 266590,China

WU Huiming,  
  • Affiliation：

    College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

    Key Laboratory of Geomatics and Digital Technology of Shandong Province, Qingdao 266590,China

BEI Yixuan,  
  • Affiliation：

    College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

    Key Laboratory of Geomatics and Digital Technology of Shandong Province, Qingdao 266590,China

LI Linye

реферат

Many airborne LiDAR point cloud filters have been proposed over the past decades. However, these existing filters are incapable of producing satisfactory results in complex landscapes, such as rugged slopes covered with low vegetation and discontinuous terrain. Thus, a point-based multi-scale morphological reconstruction filter (PMMF) is presented in this work to overcome these problems.In contrast with the classical morphological filters, PMMF takes raw point cloud rather than rasterized grids as the basic processing element. First, the potential ground points are obtained by repeatedly dilating the marker point cloud with the k-neighbor structural element and adaptive elevation buffer under the limits of the mask point cloud. Thereafter, the non-ground points mixed in the potential ground points are eliminated by a terrain-adaptive slope filter. Based on the filtering results from the upper scale, PMMF increases the grid scale for selecting ground seeds on the next scale and repeats the filtering process as the upper level until the result converges. The three main contributions of the new algorithm include a point-based morphological method rather than a grid-based one to avoid information loss caused by point cloud rasterization, a multi-scale geodesic dilation with a slope-adaptive elevation buffer to select potential ground points and reduce the omission error on a steep terrain, and a terrain-adaptive slope filter to eliminate commission errors mixed in potential ground points.PMMF was employed to filter the benchmark samples provided by ISPRS, and its results were compared with 15 filtering algorithms proposed in the last 5 years (2016—2020). Results illustrate that PMMF outperforms the other filtering methods on eight out of the 15 samples, and its average total error and Kappa coefficient were 2.71% and 91.08%, respectively. Moreover, PMMF was used to process four high-density airborne LiDAR point clouds with different terrain features, and the filtering results were compared with Progressive Morphological Filter (PMF), Cloth Simulation Filter (CSF), Progressive TIN Densification (PTD), and multiresolution hierarchical filter (MHF). Results show that PMMF with an average total error of 3.24% has the best performance. The total error of PMMF is reduced by 12.0%, 59.1%, 70.1%, and 53.2% compared with those of PMF, CSF, PTD, and MHF, respectively.A large number of experimental results show that PMMF has achieved satisfactory filtering results on various terrains, and the filtering accuracy is significantly higher than those of other conventional filtering algorithms. Experimental verification shows that the three innovations proposed in this work contribute to the higher accuracy of the new algorithm and overcome the imperfection of the existing algorithms.

ключеви́че слова́

airborne LiDAR;point cloud filtering;morphological reconstruction;geodesic dilation;adaptive slope threshold

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