A Three-Step Approach for TLS Point Cloud Classification

作者:Li, ZQ (Li, Zhuqiang)[ 1 ] ; Zhang, LQ (Zhang, Liqiang)[ 1 ] ; Tong, XH (Tong, Xiaohua)[ 2 ] ; Du, B (Du, Bo)[ 3 ] ; Wang, YB (Wang, Yuebin)[ 1 ] ; Zhang, L (Zhang, Liang)[ 1 ] ; Zhang, ZX (Zhang, Zhenxin)[ 1 ] ; Liu, H (Liu, Hao)[ 1 ] ; Mei, J (Mei, Jie)[ 1 ] ; Xing, XY (Xing, Xiaoyue)[ 1 ] ; Mathiopoulos, PT (Mathiopoulos, P. Takis)[ 4 ]

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

卷: 54  期: 9  页: 5412-5424

DOI: 10.1109/TGRS.2016.2564501

出版年: SEP 2016

摘要

The ability to classify urban objects in large urban scenes from point clouds efficiently and accurately still remains a challenging task today. A new methodology for the effective and accurate classification of terrestrial laser scanning (TLS) point clouds is presented in this paper. First, in order to efficiently obtain the complementary characteristics of each 3-D point, a set of point-based descriptors for recognizing urban point clouds is constructed. This includes the 3-D geometry captured using the spin-image descriptor computedon three different scales, the mean RGB colors of the point in the camera images, the LAB values of that mean RGB, and the normal at each 3-D point. The initial 3-D labeling of the categories in urban environments is generated by utilizing a linear support vector machine classifier on the descriptors. These initial classification results are then first globally optimized by the multilabel graph-cut approach. These results are further refined automatically by a local optimization approach based upon the object-oriented decision tree that uses weak priors among urban categories which significantly improves the final classification accuracy. The proposed method has been validated on three urban TLS point clouds, and the experimental results demonstrate that it outperforms the state-of-the-art method in classification accuracy for buildings, trees, pedestrians, and cars.

通讯作者地址: Zhang, LQ (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 ] Wuhan Univ, Comp Sch, Wuhan 430072, Peoples R China

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