LiDAR Segmentation using Suitable Seed Points for 3D Building Extraction
Effective building detection and roof reconstruction has an influential demand over the remote sensing research community. In this paper, we present a new automatic LiDAR point cloud segmentation method using suitable seed points for building detection and roof plane extraction. Firstly, the LiDAR p...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2014-08-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-3/1/2014/isprsarchives-XL-3-1-2014.pdf |
| Summary: | Effective building detection and roof reconstruction has an influential demand over the remote sensing research community. In this
paper, we present a new automatic LiDAR point cloud segmentation method using suitable seed points for building detection and roof
plane extraction. Firstly, the LiDAR point cloud is separated into "ground" and "non-ground" points based on the analysis of DEM with
a height threshold. Each of the non-ground point is marked as coplanar or non-coplanar based on a coplanarity analysis. Commencing
from the maximum LiDAR point height towards the minimum, all the LiDAR points on each height level are extracted and separated
into several groups based on 2D distance. From each group, lines are extracted and a coplanar point which is the nearest to the midpoint
of each line is considered as a seed point. This seed point and its neighbouring points are utilised to generate the plane equation. The
plane is grown in a region growing fashion until no new points can be added. A robust rule-based tree removal method is applied
subsequently to remove planar segments on trees. Four different rules are applied in this method. Finally, the boundary of each object
is extracted from the segmented LiDAR point cloud. The method is evaluated with six different data sets consisting hilly and densely
vegetated areas. The experimental results indicate that the proposed method offers a high building detection and roof plane extraction
rates while compared to a recently proposed method. |
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| ISSN: | 1682-1750 2194-9034 |