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Sensors 2016, 16(2), 140; doi:10.3390/s16020140

Segmentation of Planar Surfaces from Laser Scanning Data Using the Magnitude of Normal Position Vector for Adaptive Neighborhoods

1
Department of Civil and Environmental Engineering, College of Engineering, Myongji University, 116 Myongji-ro, Cheoin-gu, Yongin, Gyeonggy-do 449-728, Korea
2
School of Civil Engineering, Purdue University, 550 Stadium Mall Dr, West Lafayette, IN 47907, USA
3
Department of Civil Engineering, Konkuk University, Neungdong-ro, Gwangjin-gu, Seoul 143-701, Korea
4
Department of Information and Communication Engineering, Chosun University, 309 Pilmundae-ro, Dong-gu, Gwangju 501-759, Korea
5
Department of Photogrammetry, University of Bonn, Nussallee 15, 53115 Bonn, Germany
6
School of Civil and Environmental Engineering, College of Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 26 October 2015 / Revised: 15 December 2015 / Accepted: 18 January 2016 / Published: 22 January 2016
(This article belongs to the Section Physical Sensors)

Abstract

Diverse approaches to laser point segmentation have been proposed since the emergence of the laser scanning system. Most of these segmentation techniques, however, suffer from limitations such as sensitivity to the choice of seed points, lack of consideration of the spatial relationships among points, and inefficient performance. In an effort to overcome these drawbacks, this paper proposes a segmentation methodology that: (1) reduces the dimensions of the attribute space; (2) considers the attribute similarity and the proximity of the laser point simultaneously; and (3) works well with both airborne and terrestrial laser scanning data. A neighborhood definition based on the shape of the surface increases the homogeneity of the laser point attributes. The magnitude of the normal position vector is used as an attribute for reducing the dimension of the accumulator array. The experimental results demonstrate, through both qualitative and quantitative evaluations, the outcomes’ high level of reliability. The proposed segmentation algorithm provided 96.89% overall correctness, 95.84% completeness, a 0.25 m overall mean value of centroid difference, and less than 1° of angle difference. The performance of the proposed approach was also verified with a large dataset and compared with other approaches. Additionally, the evaluation of the sensitivity of the thresholds was carried out. In summary, this paper proposes a robust and efficient segmentation methodology for abstraction of an enormous number of laser points into plane information. View Full-Text
Keywords: laser scanning data; normal position vector; segmentation; planar patches; attribute homogeneity laser scanning data; normal position vector; segmentation; planar patches; attribute homogeneity
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Kim, C.; Habib, A.; Pyeon, M.; Kwon, G.-R.; Jung, J.; Heo, J. Segmentation of Planar Surfaces from Laser Scanning Data Using the Magnitude of Normal Position Vector for Adaptive Neighborhoods. Sensors 2016, 16, 140.

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