| Technology and Methodology |
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Segmentation of LiDAR point clouds based on similarity measures in multi-dimensional Euclidean Space |
| YU Liang1,4, LI Ting2, ZHAN Qingming3, YU Kun2 |
1. College of Resources and Environment, Chengdu University of Information Technology, Chengdu 615000, China;
2. China Aero Geophysical Survey and Remote Sensing Center for Land and Resources, Beijing 100083, China;
3. School of Urban Design, Wuhan University, Wuhan 430079, China;
4. Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100010, China |
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Abstract The segmentation of LiDAR point cloud is a basic and key step in 3D reconstruction of architecture. Some problems such as under-segmentation or over-segmentation exist in current point cloud segmentation based on boundary, surface or clustering method. In this paper, a point data segmentation method based on similarity measures in multi-dimension Euclidean Space(SMMES)is presented. The main workflow of this method consists of calculating point normal vector,transforming the raw data combined with image features,calculating Euclidean distance in the multi-dimension space, comparing the similarity between the adjacent points,and segmenting the point data. The method proposed in this paper has solved the problem that geometry and spectral features cannot be used in parallel during the point cloud segmentation. In addition, it has the advantages of both geo-metrical segmentation and color-metrical segmentation, and can improve the accuracy of the point cloud segmentation. The segmentation results of the three different methods which are based on geometry features, spectral features and SMMES respectively were compared with each other by using two sets of data, and the experimental results show that the proposed method is significantly feasible and practical.
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| Keywords
wetland
remote sensing(RS)
classification
land use/cover change(LUCC)
land surface temperature (LST)
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Issue Date: 01 July 2014
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2014,
Vol. 26
