Segments-based progressive TIN densification filter for DTM generation from airborne LIDAR data

作     者：许颖 Qiu Zhiwei Yue Dongjie 

作者机构：Ministry of Water Resources Key Laboratory for the Process and Control of Soil and Water Loss in Loess Plateau School of Earth Sciences and EngineeringHohai University 

出 版 物：《High Technology Letters》 (高技术通讯（英文版）)

年 卷 期：2017年第23卷第1期

页      面：16-22页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 0810[工学-信息与通信工程] 080902[工学-电路与系统] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 081105[工学-导航、制导与控制] 081001[工学-通信与信息系统] 081002[工学-信号与信息处理] 0825[工学-航空宇航科学与技术] 0811[工学-控制科学与工程] 

基　　金：Supported by the National Natural Science Foundation of China(No.41174002) the Opening Fund of Key Laboratory of the Ministry of Water Resources(No.2015003) the Fundamental Research Funds for the Central Universities(No.2014B38614) 

主　　题：airborne light detection and ranging (LIDAR) point cloud ground filtering tri-angulated irregular network (TIN) digital terrain models (DTMs) 

摘      要：Airborne light detection and ranging( LIDAR) has revolutionized conventional methods for digital terrain models( DTMs) acquisition. Ground filtering for airborne LIDAR is one of the core steps taken to obtain a high quality DTM. This paper presents a segments-based progressive TIN( triangulated irregular network) densification( SPTD) filter that can automatically separate ground points from non-ground points. The SPTD method is composed of two key steps: point cloud segmentation and clustering by iterative judgement. The clustering method uses the dual distance to obtain a set of seed points as a coarse spatial clustering process. Then the rest of the valid point clouds are classified iteratively. Finally,the datasets provided by ISPRS are utilized to test the filtering *** comparison with the commercial software Terra Solid,the experimental results show that the SPTD method in this paper can avoid single threshold restrictions. The expected accuracy of ground point determination is capable of producing reliable DTMs in the discontinuous areas.