A Multilevel Point-Cluster-Based Discriminative Feature for ALS Point Cloud Classification

作者:Zhang, ZX (Zhang, Zhenxin)[ 1 ] ; Zhang, LQ (Zhang, Liqiang)[ 1 ] ; Tong, XH (Tong, Xiaohua)[ 2 ] ; Mathiopoulos, PT (Mathiopoulos, P. Takis)[ 3 ] ; Guo, B (Guo, Bo)[ 4,5 ] ; Huang, XF (Huang, Xianfeng)[ 6 ] ; Wang, Z (Wang, Zhen)[ 1,7 ] ; Wang, YB (Wang, Yuebin)[ 1 ]

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

卷: 54  期: 6  页: 3309-3321

DOI: 10.1109/TGRS.2016.2514508

出版年: JUN 2016

摘要

Point cloud classification plays a critical role in point cloud processing and analysis. Accurately classifying objects on the ground in urban environments from airborne laser scanning (ALS) point clouds is a challenge because of their large variety, complex geometries, and visual appearances. In this paper, a novel framework is presented for effectively extracting the shape features of objects from an ALS point cloud, and then, it is used to classify large and small objects in a point cloud. In the framework, the point cloud is split into hierarchical clusters of different sizes based on a natural exponential function threshold. Then, to take advantage of hierarchical point cluster correlations, latent Dirichlet allocation and sparse coding are jointly performed to extract and encode the shape features of the multilevel point clusters. The features at different levels are used to capture information on the shapes of objects of different sizes. This way, robust and discriminative shape features of the objects can be identified, and thus, the precision of the classification is significantly improved, particularly for small objects.

通讯作者地址: Zhang, ZX (通讯作者)

Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.

地址:

[ 1 ] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China

[ 2 ] Tongji Univ, Sch Surveying & Geoinformat, Shanghai 200092, Peoples R China

[ 3 ] Natl & Kapodestrian Univ Athens, Dept Informat & Telecommun, Athens 15784, Greece

[ 4 ] Shenzhen Univ, Key Lab Geoenvironm Monitoring Coastal Zone Natl, Shenzhen 518060, Peoples R China

[ 5 ] Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing, Shenzhen 518060, Peoples R China

[ 6 ] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430072, Peoples R China

[ 7 ] China Univ Geosci, Dept Geoinformat, Beijing, Peoples R China