предыдущий|следующий

Automatic extraction of urban road boundaries using diverse LBP features

  • role： First author第一作者
  • Affiliation：

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

  • Email：liurufei@sdust.edu.cn
  • Introduction：刘如飞，研究方向为机载/车载激光点云数据处理、图像目标特征识别、三维实景GIS应用与开发。E-mail：liurufei@sdust.edu.cn
LIU Rufei1,  
  • role： Corresponding author通信作者
  • Affiliation：

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

  • Email：maxinjiang666@163.com
  • Introduction：马新江，研究方向为移动测量技术与方法。E-mail：maxinjiang666@163.com
MA Xinjiang1*,  
  • Affiliation：

    Ocean Science and Engineering College, Shandong University of Science and Technology, Qingdao 266590, China

LU Xiushan2,  
  • Affiliation：

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

WANG Minye1,  
  • Affiliation：

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

WANG Peng1

реферат

As an advanced surveying and mapping system, vehicle-borne mobile mapping system has several advantages, such as high precision, high efficiency, active, and non-contact measurement. This system can quickly collect high-precision road 3D point clouds, which are important for road boundary automatic extraction, and has become important in road information acquisition and update.To address the difficult and inaccurate extraction of urban road boundary point clouds in vehicle-borne laser point clouds, this paper introduces the Local Binary Pattern (LBP), which is an efficient and effective image processing method, to automatic point cloud classification. First, to take full advantage of the characteristics of various urban road boundaries, three improved operators were developed, including height, elevation dispersion, and spatial shape LBPs, which make full use of the three-dimensional shape, spatial geometry, and distribution characteristics of curbs. Statistical analysis was also performed on the feature image pixel values of the three LBP improvement operators. The statistical results are consistent with the spatial distribution and geometric characteristics of different objects, such as road boundary and road surface. Then, a diverse LBP features semantic recognition model, which can realize the quantitative expression of the spatial geometry and distribution characteristics curbs and pavements, was built. Finally, the road boundary point clouds were extracted by cluster and denoised with the road direction as the constraint.The point clouds of four different urban sections were tested. Results show that the extraction completeness rate of the experimental data is 92.0%. The method we developed can extract the main road and sidewalk boundary point clouds under different road environments. In terms of accuracy, 95.8% accuracy was achieved from considering the spatial distribution and geometrical characteristics of the curb. The results indicate that our method can accurately extract road boundaries in different environments and has strong adaptability.

ключеви́че слова́

remote sensing;mobile measurement system;road boundary;diverse LBP;curb;point clouds classification

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