Volume XLII-2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1157-1161, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-1157-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1157-1161, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-1157-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 May 2018

30 May 2018

INDIVIDUAL ROCKS SEGMENTATION IN TERRESTRIAL LASER SCANNING POINT CLOUD USING ITERATIVE DBSCAN ALGORITHM

A. Walicka, G. Jóźków, and A. Borkowski A. Walicka et al.
  • Institute of Geodesy and Geoinformatics, Wroclaw University of Environmental and Life Sciences, Poland

Keywords: DBSCAN, rocks segmentation, terrestrial laser scanning, fluvial transport

Abstract. The fluvial transport is an important aspect of hydrological and geomorphologic studies. The knowledge about the movement parameters of different-size fractions is essential in many applications, such as the exploration of the watercourse changes, the calculation of the river bed parameters or the investigation of the frequency and the nature of the weather events. Traditional techniques used for the fluvial transport investigations do not provide any information about the long-term horizontal movement of the rocks. This information can be gained by means of terrestrial laser scanning (TLS). However, this is a complex issue consisting of several stages of data processing. In this study the methodology for individual rocks segmentation from TLS point cloud has been proposed, which is the first step for the semi-automatic algorithm for movement detection of individual rocks. The proposed algorithm is executed in two steps. Firstly, the point cloud is classified as rocks or background using only geometrical information. Secondly, the DBSCAN algorithm is executed iteratively on points classified as rocks until only one stone is detected in each segment. The number of rocks in each segment is determined using principal component analysis (PCA) and simple derivative method for peak detection. As a result, several segments that correspond to individual rocks are formed. Numerical tests were executed on two test samples. The results of the semi-automatic segmentation were compared to results acquired by manual segmentation. The proposed methodology enabled to successfully segment 76 % and 72 % of rocks in the test sample 1 and test sample 2, respectively.