Automatic Segmentation of Raw LIDAR Data for Extraction of Building Roofs

被引:134
作者
Awrangjeb, Mohammad [1 ]
Fraser, Clive S. [2 ]
机构
[1] Monash Univ, Gippsland Sch Informat Technol, Melbourne, Vic 3842, Australia
[2] Univ Melbourne, Dept Infrastruct Engn, CRC Spatial Informat, Melbourne, Vic 3010, Australia
基金
澳大利亚研究理事会;
关键词
LIDAR; point cloud; segmentation; automatic; building; roof; extraction; MODEL RECONSTRUCTION; POINT CLOUDS; CLASSIFICATION;
D O I
10.3390/rs6053716
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Automatic extraction of building roofs from remote sensing data is important for many applications, including 3D city modeling. This paper proposes a new method for automatic segmentation of raw LIDAR (light detection and ranging) data. Using the ground height from a DEM (digital elevation model), the raw LIDAR points are separated into two groups. The first group contains the ground points that form a "building mask". The second group contains non-ground points that are clustered using the building mask. A cluster of points usually represents an individual building or tree. During segmentation, the planar roof segments are extracted from each cluster of points and refined using rules, such as the coplanarity of points and their locality. Planes on trees are removed using information, such as area and point height difference. Experimental results on nine areas of six different data sets show that the proposed method can successfully remove vegetation and, so, offers a high success rate for building detection (about 90% correctness and completeness) and roof plane extraction (about 80% correctness and completeness), when LIDAR point density is as low as four points/m(2). Thus, the proposed method can be exploited in various applications.
引用
收藏
页码:3716 / 3751
页数:36
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