ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-5/W2, 151-156, 2013
https://doi.org/10.5194/isprsannals-II-5-W2-151-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
16 Oct 2013
A novel hybrid approach for the extraction of linear/cylindrical features from laser scanning data
Z. Lari and A. Habib Department of Geomatics Engineering, University of Calgary, Calgary, Alberta, T2N 1N4, Canada
Keywords: Laser scanning, Point cloud, Feature extraction, Modelling, Segmentation, Performance Abstract. However, the collected point cloud should undergo manipulation approaches to be utilized for diverse civil, industrial, and military applications. Different processing techniques have consequently been implemented for the extraction of low-level features from this data. Linear/cylindrical features are among the most important primitives that could be extracted from laser scanning data, especially those collected in industrial sites and urban areas. This paper presents a novel approach for the identification, parameterization, and segmentation of these features in a laser point cloud. In the first step of the proposed approach, the points which belong to linear/cylindrical features are detected and their appropriate representation models are chosen based on the principal component analysis of their local neighborhood. The approximate direction and position parameters of the identified linear/cylindrical features are then refined using an iterative line/cylinder fitting procedure. A parameter-domain segmentation method is finally applied to isolate the points which belong to individual linear/cylindrical features in direction and position attribute spaces, respectively. Experimental results from real datasets will demonstrate the feasibility of the proposed approach for the extraction of linear/cylindrical features from laser scanning data.
Conference paper (PDF, 541 KB)


Citation: Lari, Z. and Habib, A.: A novel hybrid approach for the extraction of linear/cylindrical features from laser scanning data, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-5/W2, 151-156, https://doi.org/10.5194/isprsannals-II-5-W2-151-2013, 2013.

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