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| A Point-Based Fully Convolutional Neural Network for Airborne LiDAR Ground Point Filtering in Forested Environments | |
Jin, Shichao1,2; Sun, Yanjun1; Zhao, Xiaoqian1; Hu, Tianyu1; Guo, Qinghua1
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| 2020 | |
| Source Publication | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
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| ISSN | 1939-1404 |
| Volume | 13Pages:3958-3974 |
| Abstract | 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. |
| Keyword | Digital terrain model (DTM) deep learning fully convolutional neural network (FCN) ground filtering light detection and ranging (LiDAR) |
| Subject Area | Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology |
| DOI | 10.1109/JSTARS.2020.3008477 |
| Indexed By | SCI |
| Language | 英语 |
| WOS Keyword | LASER-SCANNING DATA ; MORPHOLOGICAL FILTER ; ALGORITHM ; CLASSIFICATION ; DENSIFICATION ; SEGMENTATION ; ELEVATION ; HEIGHT ; MODELS ; CLOUDS |
| WOS Research Area | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS ID | WOS:000552182800002 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Subtype | Article |
| Publication Place | PISCATAWAY |
| EISSN | 2151-1535 |
| Funding Organization | National Key R&D Program of China [2016YFC0500202, 2017YFC0503905] ; National Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [31971575, 41871332, 41901358] |
| Corresponding Author Email | jinshichao1993@gmail.com ; ysu@ibcas.ac.cn ; 7haoxiaoqian@ibcas.ac.cn ; tianyuhu@ibcas.ac.cn ; guo.qinghua@gmail.com |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ibcas.ac.cn/handle/2S10CLM1/21661 |
| Collection | 植被与环境变化国家重点实验室 |
| Affiliation | 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 |
| Recommended Citation 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. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Jin-2020-A Point-Bas（7633KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
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