太阳成8722(中国)有限公司-GREEN NO.1

标题：Automatic Vehicle Extraction from Airborne LiDAR Data Using an Object-Based Point Cloud Analysis Method作者：Zhang, Jixian; Duan, Minyan; Yan, Qin; Lin, Xiangguo

来源出版物：REMOTE SENSING 卷：6 期：9 页：8405-8423 DOI：10.3390/rs6098405 出版年：SEP 2014

摘要：Automatic vehicle extraction from an airborne laser scanning (ALS) point cloud is very useful for many applications, such as digital elevation model generation and 3D building reconstruction. In this article, an object-based point cloud analysis (OBPCA) method is proposed for vehicle extraction from an ALS point cloud. First, a segmentation-based progressive TIN (triangular irregular network) densification is employed to detect the ground points, and the potential vehicle points are detected based on the normalized heights of the non-ground points. Second, 3D connected component analysis is performed to group the potential vehicle points into segments. At last, vehicle segments are detected based on three features, including area, rectangularity and elongatedness. Experiments suggest that the proposed method is capable of achieving higher accuracy than the exiting mean-shift-based method for vehicle extraction from an ALS point cloud. Moreover, the larger the point density is, the higher the achieved accuracy is.

入藏号：WOS:000343093800023

文献类型：Article

语种：English

作者关键词：filtering, digital elevation models, point cloud segmentation, shape, connected component analysis, mean shift

扩展关键词：PROGRESSIVE TIN DENSIFICATION; URBAN AREAS; FILTER; CLASSIFICATION; SEGMENTATION; ALGORITHMS; TOPOLOGY

通讯作者地址：Duan, Minyan; Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

电子邮件地址：jxzhang@casm.ac.cn; duanmy@casm.ac.cn; yanq@sbsm.gov.cn; linxiangguo@gmail.com

地址：

[Zhang, Jixian; Duan, Minyan]Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Zhang, Jixian; Duan, Minyan; Lin, Xiangguo] Chinese Acad Surveying & Mapping, Beijing 100830, Peoples R China.

[Yan, Qin] Natl Adm Surveying Mapping & Geoinformat, Beijing 100830, Peoples R China.

研究方向：Remote Sensing

ISSN：2072-4292