A Point-Based Fully Convolutional Neural Network for Airborne LiDAR Ground Point Filtering in Forested Environments

doi:10.1109/JSTARS.2020.3008477

CORC > 植物研究所 > 中国科学院植物研究所 > 植被与环境变化国家重点实验室

	A Point-Based Fully Convolutional Neural Network for Airborne LiDAR Ground Point Filtering in Forested Environments
	Jin, Shichao 1,2; Sun, Yanjun 1; Zhao, Xiaoqian 1; Hu, Tianyu 1; Guo, Qinghua 1
刊名	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
	2020
卷号	13 页码:3958-3974
关键词	Digital terrain model (DTM) deep learning fully convolutional neural network (FCN) ground filtering light detection and ranging (LiDAR)
ISSN号	1939-1404
DOI	10.1109/JSTARS.2020.3008477
文献子类	Article
英文摘要	Airborne laser scanning (ALS) data is one of the most commonly used data for terrain products generation. Filtering ground points is a prerequisite step for ALS data processing. Traditional filtering methods mainly use handcrafted features or predefined classification rules with preprocessing/post-processing operations to filter ground points iteratively, which is empirical and cumbersome. Deep learning provides a new approach to solve classification and segmentation problems because of its ability to self-learn features, which has been favored in many fields, particularly remote sensing. In this article, we proposed a point-based fully convolutional neural network (PFCN) which directly consumed points with only geometric information and extracted both point-wise and tile-wise features to classify each point. The network was trained with 37449157 points from 14 sites and evaluated on 6 sites in various forested environments. Additionally, the method was compared with five widely used filtering methods and one of the best point-based deep learning methods (PointNet++). Results showed that the PFCN achieved the best results in terms of mean omission error (T1 = 1.10%), total error (Te = 1.73%), and Kappa coefficient (93.88%), but ranked second for the root mean square error of the digital Terrain model caused by the worst commission error. Additionally, our method was on par with or even better than PointNet++ in accuracy. Moreover, the method consumes one-third of the computational resource and one-seventh of the training time. We believe that PFCN is a simple and flexible method that can be widely applied for ground point filtering.
学科主题	Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
电子版国际标准刊号	2151-1535
出版地	PISCATAWAY
WOS关键词	LASER-SCANNING DATA ; MORPHOLOGICAL FILTER ; ALGORITHM ; CLASSIFICATION ; DENSIFICATION ; SEGMENTATION ; ELEVATION ; HEIGHT ; MODELS ; CLOUDS
WOS研究方向	Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000552182800002
资助机构	National Key R&D Program of China [2016YFC0500202, 2017YFC0503905] ; National Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [31971575, 41871332, 41901358]
内容类型	期刊论文
源URL	[http://ir.ibcas.ac.cn/handle/2S10CLM1/21661]
专题	植被与环境变化国家重点实验室
作者单位	1.Nanjing Agr Univ, Plant Phen Res Ctr, Nanjing 210095, Peoples R China 2.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Jin, Shichao,Sun, Yanjun,Zhao, Xiaoqian,et al. A Point-Based Fully Convolutional Neural Network for Airborne LiDAR Ground Point Filtering in Forested Environments[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2020,13:3958-3974.
APA	Jin, Shichao,Sun, Yanjun,Zhao, Xiaoqian,Hu, Tianyu,&Guo, Qinghua.(2020).A Point-Based Fully Convolutional Neural Network for Airborne LiDAR Ground Point Filtering in Forested Environments.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,13,3958-3974.
MLA	Jin, Shichao,et al."A Point-Based Fully Convolutional Neural Network for Airborne LiDAR Ground Point Filtering in Forested Environments".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 13(2020):3958-3974.