Title
Building Extraction From Airborne Lidar Data Based On Multi-Constraints Graph Segmentation
Abstract
Building extraction from airborne Light Detection and Ranging (LiDAR) point clouds is a significant step in the process of digital urban construction. Although the existing building extraction methods perform well in simple urban environments, when encountering complicated city environments with irregular building shapes or varying building sizes, these methods cannot achieve satisfactory building extraction results. To address these challenges, a building extraction method from airborne LiDAR data based on multi-constraints graph segmentation was proposed in this paper. The proposed method mainly converted point-based building extraction into object-based building extraction through multi-constraints graph segmentation. The initial extracted building points were derived according to the spatial geometric features of different object primitives. Finally, a multi-scale progressive growth optimization method was proposed to recover some omitted building points and improve the completeness of building extraction. The proposed method was tested and validated using three datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS). Experimental results show that the proposed method can achieve the best building extraction results. It was also found that no matter the average quality or the average F1 score, the proposed method outperformed ten other investigated building extraction methods.
Year
DOI
Venue
2021
10.3390/rs13183766
REMOTE SENSING
Keywords
DocType
Volume
airborne LiDAR, building extraction, graph segmentation, object primitive, geometric feature
Journal
13
Issue
Citations 
PageRank 
18
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Zhenyang Hui101.35
Zhuoxuan Li200.68
Penggen Cheng311.38
Yao Ziggah Yevenyo400.34
JunLin Fan500.34