Volume XLII-2/W7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 703-709, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-703-2017
© 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, 703-709, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-703-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  13 Sep 2017

13 Sep 2017

AN AUTOMATIC FILTER ALGORITHM FOR DENSE IMAGE MATCHING POINT CLOUDS

Y. Q. Dong1,2, L. Zhang2, X. M. Cui1, and H. B. Ai2 Y. Q. Dong et al.
  • 1College of Geoscience and Surveying Engineering, China University of Mining and Technology(Bei Jing), Beijing 100083, China
  • 2Chinese Academy of Surveying and Mapping, Beijing 100830,China

Keywords: Filtering, Dense Image Matching Point Clouds, Density, Angles, TIN

Abstract. Although many filter algorithms have been presented over past decades, these algorithms are usually designed for the Lidar point clouds and can’t separate the ground points from the DIM (dense image matching, DIM) point clouds derived from the oblique aerial images owing to the high density and variation of the DIM point clouds completely. To solve this problem, a new automatic filter algorithm is developed on the basis of adaptive TIN models. At first, the differences between Lidar and DIM point clouds which influence the filtering results are analysed in this paper. To avoid the influences of the plants which can’t be penetrated by the DIM point clouds in the searching seed pointes process, the algorithm makes use of the facades of buildings to get ground points located on the roads as seed points and construct the initial TIN. Then a new densification strategy is applied to deal with the problem that the densification thresholds do not change as described in other methods in each iterative process. Finally, we use the DIM point clouds located in Potsdam produced by Photo-Scan to evaluate the method proposed in this paper. The experiment results show that the method proposed in this paper can not only separate the ground points from the DIM point clouds completely but also obtain the better filter results compared with TerraSolid. 1.