地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (7): 869-877.doi: 10.3724/SP.J.1047.2016.00869

• 地球信息科学理论与方法 •    下一篇

面向点云数据的黄土丘陵沟壑区沟沿线自动提取方法

李敏1,2(), 杨昕1,2,*(), 陈盼盼3, 熊礼阳1,2   

  1. 1. 南京师范大学 虚拟地理环境教育部重点实验室,南京 210023
    2. 江苏省地理信息资源开发与利用协同创新中心,南京 210023
    3. 河南省科学院地理研究所,郑州 450052
  • 收稿日期:2015-11-30 修回日期:2016-01-25 出版日期:2016-07-15 发布日期:2016-07-19
  • 通讯作者: 杨昕 E-mail:limin2652035@163.com;xxinyang@163.com
  • 作者简介:

    作者简介:李敏(1992-),女,江西九江人,硕士生,研究方向为数字地形分析和三维激光扫描数据处理。E-mail: limin2652035@163.com

  • 基金资助:
    国家自然科学基金项目“基于DEM的黄土沟壑种群特征及空间异质性研究”(41271438)、“基于山顶点群的陕西省地貌形态空间分异研究”(41201414);江苏高校优势学科建设工程资助项目(64320H116)

Method of Automatic Shoulder Line Extraction in the Loess Hilly Area Based on Point Cloud Data

LI Min1,2(), YANG Xin1,2,*(), CHEN Panpan3, XIONG Liyang1,2   

  1. 1. Key Laboratory of Virtual Geographic Environment of Ministry of Education, Nanjing Normal University, Nanjing 210023, China
    2. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
    3. Institute of Geographical Sciences, Henan Academy of Sciences, Zhengzhou 450052, China
  • Received:2015-11-30 Revised:2016-01-25 Online:2016-07-15 Published:2016-07-19
  • Contact: YANG Xin E-mail:limin2652035@163.com;xxinyang@163.com

摘要:

基于点云数据地表建模中,沟沿线既是点云去噪的重要分割线,又是黄土丘陵沟壑区最典型的地形特征线。因此,本文提出了面向点云数据的基于多尺度格网采样和坡度分割的沟沿线自动提取方法,即在适宜格网尺度采样的基础上构建地表模型,利用沟沿线周围坡度陡变的特性提取缓坡面与陡坡面的分界线,从而生成沟沿线。通过对8个样区反复实验,确定了各自的最佳格网尺度,并发现格网尺度随点密度的增大而迅速减小,而后趋近平稳的幂函数关系,同时随机选取了另外5个样区验证了该关系的适用性。最后,通过全域分块计算得到了该地区的完整沟沿线。相比于人工识别的沟沿线,本文方法提取沟沿线的精度在0.5 m缓冲范围内为85%,效果较好,且位置更为精确。产生差异的原因主要是面向点云数据的沟沿线有更多的细节,沟沿线曲率更大,造成了其长度明显大于手动提取结果。该方法有助于提高丘陵沟壑区点云数据去植被处理和地形表面重建精度。

关键词: 点云, 地面三维激光扫描, 沟沿线, DEM, 黄土丘陵沟壑

Abstract:

Shoulder line is one of the most distinguishing terrain structure lines in Loess Plateau. However, in the loess hilly area, the point cloud near the shoulder line is usually erased mistakenly while using the unified vegetation removal algorithm, which could result in the decrease of DEM accuracy in the terrain expression. Thus the shoulder line should be extracted firstly before removing the vegetation. This paper has proposed a novel shoulder line extraction method based on point cloud data using the multi-scale sampling and slope threshold segmentation. Firstly, a surface model was built based on the ground points selected by a proper filter window. Then, based on the constructed surface model, the slope was calculated and a significant difference near the shoulder line would emerge. Finally, the shoulder line was generated using the slope threshold segmentation. Through the multi-iterative experiments, the optimal filter sizes for each of the 8 sample areas were found, and a power function between the filter size and the point density was discovered. The correctness and reasonability of this correlation was verified in another 5 test areas. Then, the shoulder line of the whole area was generated by using this verified correlation. Using the overlay analysis over the manual identification result, the extracted shoulder line has an accuracy of 85% (within a 0.5 m buffer area). As a conclusion, this method could contribute to improve the accuracy of vegetation removal algorithm that uses the point cloud data for the loess hilly area.

Key words: point cloud, terrestrial 3D laser scanner, shoulder line, DEM, loess hilly area