سابِق|تال

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

ملخص

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.

مفهوم

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

References

  1. 1.
    Bastani F, He S T, Abbar S, Alizadeh M, Balakrishnan H, Chawla S, Madden S and DeWitt D. 2018. Roadtracer: automatic extraction of road networks from aerial images//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE: 4720-4728
  2. 2.
    Dai J G, Du Y, Fang X X, Wang Y and Miao Z P. 2018. Road extraction method for high resolution optical remote sensing images with multiple feature constraints. Journal of Remote Sensing, 22(5): 777-791
  3. 3.
    Han T, Yang B S, Yuan P F and Liang F X. 2018. OSM-assisted extraction of 3D vector boundary from mobile laser scanning point cloud. Geomatics Science and Technology, 6(2): 128-140
  4. 4.
    Hui Z Y and Hu Y J. 2017. A review on road extraction methods from airborne LiDAR. Science of Surveying and Mapping, 42(3): 70-74
  5. 5.
    Ibrahim S and Lichti D. 2012. Curb-based street floor extraction from mobile terrestrial LIDAR point cloud. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIX-B5: 193-198
  6. 6.
    Jaakkola A, Hyyppä J, Hyyppä H and Kukko A. 2008. Retrieval algorithms for road surface modelling using laser-based mobile mapping. Sensors, 8(9): 5238-5249
  7. 7.
    Li C and Li F F. 2013. Auto-extracting sub-pixel line feature of digital images based on hypothesis testing. Acta Geodaetica et Cartographica Sinica, 42(1): 67-72
  8. 8.
    Li C and Wei D. 2016. A line segment detector based on pixels grouping and step-by-step quality control. Journal of Infrared and Millimeter Waves, 35(6): 681-687
  9. 9.
    Li C, Zhang Y J and Zhang Z X. 2016. Automatic keyline recognition and 3D reconstruction for quasi-planar façades in close-range images. The Photogrammetric Record, 31(153): 29-50
  10. 10.
    Li L, Zhang Y S, Yu Y, Xue W and Xie L M. 2017. Urban road extraction from airborne LiDAR point cloud automatically based on skewness balance algorithm. Journal of Geomatics Science and Technology, 34(6): 628-632, 638
  11. 11.
    Li X, Zhang W G and Bian X D. 2004. Research on detection of lane based on machine vision. Journal of Southeast University, 20(2): 176-180
  12. 12.
    Liu R F, Lu X S, Yue G W and Tian M Y. 2017. An automatic extraction method of road from vehicle-borne laser scanning point clouds. Geomatics and Information Science of Wuhan University, 42(2): 250-256
  13. 13.
    Luo H F, Fang L N and Chen C C. 2017. Curb point clouds extraction from vehicle-borne laser scanning data. Journal of Geo-information Science, 19(7): 861-871
  14. 14.
    Ojala T, Pietikäinen M and Harwood D. 1996. A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, 29(1): 51-59
  15. 15.
    Ojala T, Pietikainen M and Maenpaa T. 2002. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7): 971-987
  16. 16.
    Serna A and Marcotegui B. 2013. Urban accessibility diagnosis from mobile laser scanning data. ISPRS Journal of Photogrammetry and Remote Sensing, 84: 23-32
  17. 17.
    Song K C, Yan Y H, Chen W H and Zhang X. 2013. Research and perspective on local binary pattern. Acta Automatica Sinica, 39(6): 730-744
  18. 18.
    Yang B S, Fang L N and Li J. 2013. Semi-automated extraction and delineation of 3D roads of street scene from mobile laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 79: 80-93
  19. 19.
    Yang B S, Liu Y, Dong Z, Liang F X, Li B J, Peng X Y. 2017. 3D local feature BKD to extract road information from mobile laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 130: 329-343
  20. 20.
    Yang W S, Cai L L and Gu S D. 2018. Extraction city road boundary method based on point cloud normal vector clustering. Acta Photonica Sinica, 47(6): 0612003
  21. 21.
    Yu Y T, Li J, Guan H Y, Wang C and Yu J. 2015. Semiautomated extraction of street light poles from mobile LiDAR point-clouds. IEEE Transactions on Geoscience and Remote Sensing, 53(3): 1374-1386
  22. 22.
    Zai D W, Li J, Guo Y L, Cheng M, Lin Y B, Luo H and Wang C. 2018. 3-D road boundary extraction from mobile laser scanning data via supervoxels and graph cuts. IEEE Transactions on Intelligent Transportation Systems, 19(3): 802-813
  23. 23.
    Zhang J X, Lin X G and Liang X L. 2017. Advances and prospects of information extraction from point clouds. Acta Geodaetica et Cartographica Sinica, 46(10): 1460-1469
  24. 24.
    Zhang W D. 2010. LIDAR-based road and road-edge detection//Proceedings of 2010 IEEE Intelligent Vehicles Symposium. San Diego: IEEE: 845-848
  25. 25.
    Zhang Y, Heipke C, Butenuth M and Hu X. 2006. Automatic extraction of wind erosion obstacles by integration of GIS data, DSM and stereo images. International Journal of Remote Sensing, 27(8): 1677-1690
  26. 26.
    Zhou L and Vosselman G. 2012. Mapping curbstones in airborne and mobile laser scanning data. International Journal of Applied Earth Observation and Geoinformation, 18: 293-304

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