Volume XLII-2/W7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 291-295, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-291-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 291-295, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-291-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  12 Sep 2017

12 Sep 2017

CSF BASED NON-GROUND POINTS EXTRACTION FROM LIDAR DATA

A. Shen, W. Zhang, and H. Shi A. Shen et al.
  • State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

Keywords: Region growing; seed point; CSF; non-ground point; cloth resolution

Abstract. Region growing is a classical method of point cloud segmentation. Based on the idea of collecting the pixels with similar properties to form regions, region growing is widely used in many fields such as medicine, forestry and remote sensing. In this algorithm, there are two core problems. One is the selection of seed points, the other is the setting of the growth constraints, in which the selection of the seed points is the foundation. In this paper, we propose a CSF (Cloth Simulation Filtering) based method to extract the non-ground seed points effectively. The experiments have shown that this method can obtain a group of seed spots compared with the traditional methods. It is a new attempt to extract seed points