International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-3/W10
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 221–228, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-221-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 221–228, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-221-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Feb 2020

07 Feb 2020

LIDAR DATA FILTERING BASED ON THE IMPROVED WINDOW SIZE OF PROGRESSIVE MATHEMATICAL MORPHOLOGY

X. J. Lu, L. Luo, B. Zhou, Y. H. Huang, and C. L. Wu X. J. Lu et al.
  • College of Geomatics and Geoinformation, Guilin University of Technology, Guilin Guangxi 541004, China

Keywords: LiDAR, point cloud, morphology filtering, opening operation, window size, topographic features

Abstract. Filtering is one of the core post-processing steps of airborne LiDAR point cloud data. It is difficult for traditional mathematical morphology filtering algorithms to preserve sudden terrain features, especially when using larger filtering windows. In this paper, an improved progressive mathematical morphology filtering algorithm is proposed to solve the problem which is difficult to filter out a large area of non-ground points effectively and causes omission filtering on prominent topographic features. First the elevation information of point cloud data is meshed, and then the opening operation (erosion and dilation) is performed. By improving the mathematical formula of window size, the window size and the corresponding elevation difference threshold are iterated continuously. Within each corresponding filtering window, objects that are larger than the size of the structural element window are retained, and objects smaller than the size of the structural element window are filtered. Fourteen samples provided by ISPRS committee were selected to test the performance of the proposed method. Experimental results show that the improved method can effectively filter out most of the non-ground points, and this method can achieve great results not only in urban flat areas, but also in the mountains. Compared with the traditional filtering methods, the filter performance of the new method proposed in this paper has been greatly improved. The method in this paper obtains the lower errors and retains the complex topographic features.