Color point cloud classification of mine surface

doi:10.13474/j.cnki.11-2246.2020.0145

Abstract

Abstract: Taking the color 3D laser point cloud data of the mining area as the research object, a fast automatic classification and target extraction method for the point cloud of the mining area is proposed. Firstly, the H value in HSV space is calculated according to the RGB value of color point cloud. According to the difference of H value among different objects, the points of ground point and non ground point are extracted according to the prior value of ground object color. Then, the extracted points are clustered and calculated. Using the significant difference in spatial distribution of all kinds of ground object point clouds, layered cross-section projection is adopted, and the ratio of length to width of the minimum bounding box of projection points is used In this paper. Finally the RIEGL vz-1000 scanner is used as the experimental object to verify the feasibility and practicability of this algorithm.

Key words: ground laser scanning technology, RGB, HSV, point cloud classification, stratified section

CLC Number:

P234

CAI Lailiang, SONG Deyun, WEI Fengyuan, XUE Yuan, SHU Qianjin. Color point cloud classification of mine surface[J]. Bulletin of Surveying and Mapping, 2020, 0(5): 55-58.

References

[1] 张爱武,李文宁,段乙好,等. 结合点特征直方图的点云分类方法[J]. 计算机辅助设计与图形学学报, 2016,28(5):795-801.
[2] 左志权,张祖勋,张剑清. 区域回波比率与拓扑识别模型结合的城区激光雷达点云分类方法[J]. 中国激光, 2012,39(4):189-194.
[3] 郭波,黄先锋,张帆,等. 顾及空间上下文关系的JointBoost点云分类及特征降维[J]. 测绘学报, 2013,42(5):715-721.
[4] ZHANG X Y, DU S H, WANG Y C. Semantic classification of heterogeneous urban scenes using intrascene feature similarity and interscene semantic dependency[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(5):2005-2014.
[5] 杨必胜,魏征,李清泉,等. 面向车载激光扫描点云快速分类的点云特征图像生成方法[J]. 测绘学报, 2010, 39(5):540-545.
[6] CHARANIYA A P, MANDUCHI R, LODHA S K. Super-vised parametric classification of aerial LiDAR data[C]//Proceedings of Computer Vision and Pattern Recognition Workshop. Washington:IEEE, 2004.
[7] 李海亭,肖建华,李艳红,等. 机器学习在车载激光点云分类中的应用研究[J]. 华中师范大学学报(自然科学版), 2015,45(3):460-464.
[8] 田巍,庄镇泉. 基于HSV色彩空间的自适应肤色检测[J]. 计算机工程与应用, 2004,40(14):81-85.
[9] 杨必胜,梁福逊,黄荣刚. 三维激光扫描点云数据处理研究进展、挑战与趋势[J]. 测绘学报, 2017, 46(10):1509-1516.
[10] 刘明君,董增寿,邵贵成. 基于改进的四叉树分解多聚焦图像融合算法研究[J]. 科技通报, 2019, 35(4):152-156.
[11] 李明磊,李广云,王力,等. 采用八叉树体素生长的点云平面提取[J]. 光学精密工程, 2018, 26(1):172-183.
[12] 张蕊,李广云,王力,等. 车载LiDAR点云混合索引新方法[J]. 武汉大学学报(信息科学版), 2018, 43(7):993-999.
[13] 杨建思. 一种四叉树与KD树结合的海量机载LiDAR数据组织管理方法[J]. 武汉大学学报(信息科学版), 2014, 39(8):918-922.
[14] 朱清海. 一种基于八叉树车载激光点云的杆式地物批量提取方法[J]. 测绘通报, 2019(S2):110-111.
[15] 董亚涵,李永强,孙渡,等. 基于车载LiDAR数据的行道树信息提取与动态分析[J]. 地理与地理信息科学, 2018, 34(4):46-51,82.
[16] ZHANG W M, Qi J B, PENG W, et al. An easy-to-use airborne LiDAR data filtering method based on cloth simulation[J]. Remote Sensing, 2016, 8(6):501.