Volume XL-2/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-2/W3, 187-190, 2014
https://doi.org/10.5194/isprsarchives-XL-2-W3-187-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-2/W3, 187-190, 2014
https://doi.org/10.5194/isprsarchives-XL-2-W3-187-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

  22 Oct 2014

22 Oct 2014

SPATIAL ANALYSIS FOR OUTLIER REMOVAL FROM LIDAR DATA

A. A. Matkan1, M. Hajeb1, B. Mirbagheri1, S. Sadeghian2, and M. Ahmadi1 A. A. Matkan et al.
  • 1Department of Remote Sensing & GIS, Shahid Beheshti University, Evin, Tehran, Iran
  • 2Geomatics College of National Cartographic Center, Meraj street, Azadi square, Tehran, Iran

Keywords: Spatial analysis, Cross-validation, LiDAR, Outlier detection

Abstract. Outlier detection in LiDAR point clouds is a necessary process before the subsequent modelling. So far, many studies have been done in order to remove the outliers from LiDAR data. Some of the existing algorithms require ancillary data such as topographic map, multiple laser returns or intensity data which may not be available, and some deal only with the single isolated outliers. This is an attempt to present an algorithm to remove both the single and cluster types of outliers, by exclusively use of the last return data. The outliers will be removed by spatial analyzing of LiDAR point clouds in a hierarchical scheme that is uses a cross-validation technique. The algorithm is tested on a dataset including many single and cluster outliers. Our algorithm can deal with both the irregular LiDAR point clouds and the regular grid data. Experimental results show that the presented algorithm almost completely detects both the single and cluster outliers, but some inlier points are wrongly removed as outlier. An accuracy assessment indicated 0.018% Error α and, 0.352% Error β that are very satisfactory.