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Selected Papers from The Sixth National LiDAR Conference

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 31159

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Special Issue Editors

Prof. Dr. Ruofei Zhong

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Guest Editor

Key Lab of 3-Dimensional Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China
Interests: remote sensing satellite design and data preprocessing; multisensor lidar integration and post-processing; deep learning and computer vision

Prof. Dr. Zhizhong Kang

E-Mail Website
Guest Editor

Department of Remote Sensing and Geo-Information Engineering, School of Land Science and Technology, China University of Geosciences in Beijing, Xueyuan Road 29, Haidian District, Beijing 100083, China
Interests: digital photogrammetry and computer vision; processing of indoor, terrestrial and air-borne LiDAR data; indoor 3D modeling
Special Issues, Collections and Topics in MDPI journals

Dr. Xiaohuan Xi

E-Mail Website
Guest Editor

Key Lab of Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: LiDAR data processing; LiDAR application in vegetation; 3D digital reconstruction; LiDAR and multiple sources data fusion

Dr. Haili Sun

E-Mail Website
Guest Editor

Key Lab of 3-Dimensional Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China
Interests: LiDAR point cloud processing; multisensor integration and its engineering application; intelligent detection of rail transit

Dr. Jinhu Wang

E-Mail Website
Guest Editor

Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: mobile laser scanning; point cloud data; high-definition 3D digital mapping; scene understanding

Special Issue Information

Dear Colleagues,

The Sixth National LiDAR Conference (https://chinalidar2020.cugb.edu.cn) will be held during 20-22 November 2020 in Beijing, China. It is our pleasure to invite you to submit papers to this Special Issue titled “Selected Papers from The Sixth National LiDAR Conference”.

The focus of this conference is on promoting advanced applications of LiDAR remote sensing technology and summarizing recent achievements of research in China. The joint event aims to bring together researchers, professionals, and students from different domains to discuss and exchange ideas and to demonstrate new developments of basic theories, new technologies, and advanced applications in the field of LiDAR and other related topics. It will provide great opportunities to develop new techniques for LiDAR systems and equipments, to improve innovative theories and methods of LiDAR data processing, such as 3D modeling, segmentation, and classification, and target recognition and extraction, as well as to exploit and promote the industry comprehensive applications of LiDAR. Much attention will be given to key technical issues and possible new science directions of LiDAR technology. Best practices on geoscience using LiDAR technology will be showcased.

We therefore encourage experts and scholars who are engaged in relevant research fields to submit their work in this Special Issue. Note that contributions must be original academic achievements and must have not been submitted or published in other journals or conferences. Topics include but are not limited to the following:

LiDAR system and equipment

LiDAR system and development
LiDAR light source
LiDAR detection
Multispectral LiDAR system
Single photon LiDAR system
Low cost RGB-D distance sensor
LiDAR components and equipment

Theory and method of LiDAR data processing

LiDAR data processing methods
LiDAR information extraction methods
LiDAR 3D modeling technology and methods
Full waveform LiDAR data analysis
Point cloud segmentation and classification
Point cloud target recognition and extraction
Point cloud semantic annotation
Point cloud change detection
LiDAR and multisource data registration/fusion technology and methods.

Geoscience LiDAR remote sensing and its applications

Mobile LiDAR systems and applications
LiDAR multiecho geoscience information extraction
Airborne LiDAR surveying and 3D modeling
Shipborne LiDAR system and ocean mapping
Indoor LiDAR system and data processing

Data processing and application of satellite laser altimetry

Satellite laser altimeter systems
The construction of satellite laser altimeter geometric models and error analysis
High-precision geometric calibration of satellite laser altimeters
Data applications of satellite laser altimetry
Large-spot LiDAR full waveform data processing
Data processing of spaceborne photon counting LiDAR

Industry comprehensive applications of LiDAR

Surveying and mapping, 2D and 3D mapping applications
Forestry applications
Power applications
Traffic applications
Polar and marine applications
Indoor application
Atmospheric environment applications
Emergency services, disaster prevention and mitigation
Other industry applications

Prof. Dr. Ruofei Zhong
Prof. Dr. Zhizhong Kang
Dr. Xiaohuan Xi
Dr. Haili Sun
Dr. Jinhu Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

LiDAR system and equipment
theory and method of LiDAR data processing
geoscience LiDAR remote sensing and its applications
data processing and application of satellite laser altimetry
industry comprehensive applications of LiDAR

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Published Papers (8 papers)

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Research

22 pages, 4538 KiB

Open AccessArticle

Railway Overhead Contact System Point Cloud Classification

by Xiao Chen, Zhuang Chen, Guoxiang Liu, Kun Chen, Lu Wang, Wei Xiang and Rui Zhang

Sensors 2021, 21(15), 4961; https://doi.org/10.3390/s21154961 - 21 Jul 2021

Cited by 11 | Viewed by 4211

Abstract

As the railway overhead contact system (OCS) is the key component along the high-speed railway, it is crucial to detect the quality of the OCS. Compared with conventional manual OCS detection, the vehicle-mounted Light Detection and Ranging (LiDAR) technology has advantages such as high efficiency and precision, which can solve the problems of OCS detection difficulty, low efficiency, and high risk. Aiming at the contact cables, return current cables, and catenary cables in the railway vehicle-mounted LiDAR OCS point cloud, this paper used a scale adaptive feature classification algorithm and the DBSCAN (density-based spatial clustering of applications with noise) algorithm considering OCS characteristics to classify the OCS point cloud. Finally, the return current cables, catenary cables, and contact cables in the OCS were accurately classified and extracted. To verify the accuracy of the method presented in this paper, we compared the experimental results of this article with the classification results of TerraSolid, and the classification results were evaluated in terms of four accuracy indicators. According to statistics, the average accuracy of using this method to extract two sets of OCS point clouds is 99.83% and 99.89%, respectively; the average precision is 100% and 99.97%, respectively; the average recall is 99.16% and 99.42%, respectively; and the average overall accuracy is 99.58% and 99.69% respectively, which is overall better than TerraSolid. The experimental results showed that this approach could accurately and quickly extract the complete OCS from the point cloud. It provides a new method for processing railway OCS point clouds and has high engineering application value in railway component detection. Full article

(This article belongs to the Special Issue Selected Papers from The Sixth National LiDAR Conference)

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19 pages, 6935 KiB

Open AccessArticle

Method for Tunnel Displacements Calculation Based on Mobile Tunnel Monitoring System

by Zeyu Yue, Haili Sun, Ruofei Zhong and Liming Du

Sensors 2021, 21(13), 4407; https://doi.org/10.3390/s21134407 - 27 Jun 2021

Cited by 23 | Viewed by 3659

Abstract

Efficient, high-precision, and automatic measurement of tunnel structural changes is the key to ensuring the safe operation of subways. Conventional manual, static, and discrete measurements cannot meet the requirements of rapid and full-section detection in subway construction and operation. Mobile laser scanning technology is the primary method for tunnel detection. Herein, we propose a method to calculate shield tunnel displacements of a full cross-section tunnel. The point cloud data, obtained via a mobile tunnel deformation detection system, were fitted, projected, and interpolated to generate an orthophoto image. Combined with the cumulative characteristics of the tunnel gray gradient, the longitudinal ring seam of the tunnel was identified, while the Canny algorithm and Hough line detection algorithm identified the transverse seam. The symmetrical vertical foot method and cross-section superposition analysis were used to calculate the circumferential and radial displacements, respectively. The proposed displacement calculation method achieves automatic recognition of a ring seam, reduces human–computer interaction, and is fast, intelligent, and accurate. Furthermore, the description of the tunnel deformation location and deformation amount is more quantitative and specific. These results confirm the significance of shield tunnel displacement monitoring based on mobile monitoring systems in tunnel disease monitoring. Full article

(This article belongs to the Special Issue Selected Papers from The Sixth National LiDAR Conference)

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19 pages, 4221 KiB

Open AccessArticle

Three-Dimensional Linear Restoration of a Tunnel Based on Measured Track and Uncontrolled Mobile Laser Scanning

by Yulong Han, Haili Sun and Ruofei Zhong

Sensors 2021, 21(11), 3815; https://doi.org/10.3390/s21113815 - 31 May 2021

Cited by 12 | Viewed by 2984

Abstract

Traditional precision measurement adopts discrete artificial static observation, which cannot meet the demands of the dynamic, continuous, fine and high-precision holographic measurement of large-scale infrastructure construction and complex operation and maintenance management. Due to its advantages of fast, accurate and convenient measurement, mobile laser scanning technology is becoming a popular technology in the maintenance and measurement of infrastructure construction such as tunnels. However, in some environments without satellite signals, such as indoor areas and underground spaces, it is difficult to obtain 3D data by means of mobile measurement technology. This paper proposes a method to restore the linear of the point cloud obtained by mobile laser scanning based on the measured track center line. In this paper, the measured track position is interpolated with a cubic spline to calculate the translations, and the rotation parameters are calculated by combining the simulation design data. The point cloud of the cross-section of the tunnel under the local coordinate system is converted to the absolute coordinate system to calculate the tunnel line. In addition, the method is verified by experiments combined with the subway tunnel data, and the overall point error can be controlled to within 0.1 m. The average deviation in the horizontal direction is 0.0551 m, and that in the vertical direction is 0.0274 m. Compared with the previous methods, this method can effectively avoid the obvious deformation of the tunnel and the sharp increase in the error, and can process the tunnel point cloud data more accurately and quickly. It also provides better data support for subsequent tunnel analysis such as 3D display, completion survey, systematic hazard management and so on. Full article

(This article belongs to the Special Issue Selected Papers from The Sixth National LiDAR Conference)

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25 pages, 11481 KiB

Open AccessArticle

Analysis and Radiometric Calibration for Backscatter Intensity of Hyperspectral LiDAR Caused by Incident Angle Effect

by Wenxin Tian, Lingli Tang, Yuwei Chen, Ziyang Li, Jiajia Zhu, Changhui Jiang, Peilun Hu, Wenjing He, Haohao Wu, Miaomiao Pan, Jing Lu and Juha Hyyppä

Sensors 2021, 21(9), 2960; https://doi.org/10.3390/s21092960 - 23 Apr 2021

Cited by 11 | Viewed by 3561

Abstract

Hyperspectral LiDAR (HSL) is a new remote sensing detection method with high spatial and spectral information detection ability. In the process of laser scanning, the laser echo intensity is affected by many factors. Therefore, it is necessary to calibrate the backscatter intensity data of HSL. Laser incidence angle is one of the important factors that affect the backscatter intensity of the target. This paper studied the radiometric calibration method of incidence angle effect for HSL. The reflectance of natural surfaces can be simulated as a combination of specular reflection and diffuse reflection. The linear combination of the Lambertian model and Beckmann model provides a comprehensive theory that can be applied to various surface conditions, from glossy to rough surfaces. Therefore, an adaptive threshold radiometric calibration method (Lambertian–Beckmann model) is proposed to solve the problem caused by the incident angle effect. The relationship between backscatter intensity and incident angle of HSL is studied by combining theory with experiments, and the model successfully quantifies the difference between diffuse and specular reflectance coefficients. Compared with the Lambertian model, the proposed model has higher calibration accuracy, and the average improvement rate to the samples in this study was 22.67%. Compared with the results before calibration with the incidence angle of less than 70°, the average improvement rate of the Lambertian–Beckmann model was 62.26%. Moreover, we also found that the green leaves have an obvious specular reflection effect near 650–720 nm, which might be related to the inner microstructure of chlorophyll. The Lambertian–Beckmann model was more helpful to the calibration of leaves in the visible wavelength range. This is a meaningful and a breakthrough exploration for HSL. Full article

(This article belongs to the Special Issue Selected Papers from The Sixth National LiDAR Conference)

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20 pages, 5203 KiB

Open AccessArticle

A Strip Adjustment Method of UAV-Borne LiDAR Point Cloud Based on DEM Features for Mountainous Area

by Zequan Chen, Jianping Li and Bisheng Yang

Sensors 2021, 21(8), 2782; https://doi.org/10.3390/s21082782 - 15 Apr 2021

Cited by 14 | Viewed by 4293

Abstract

Due to the trajectory error of the low-precision position and orientation system (POS) used in unmanned aerial laser scanning (ULS), discrepancies usually exist between adjacent LiDAR (Light Detection and Ranging) strips. Strip adjustment is an effective way to eliminate these discrepancies. However, it is difficult to apply existing strip adjustment methods in mountainous areas with few artificial objects. Thus, digital elevation model-iterative closest point (DEM-ICP), a pair-wise registration method that takes topography features into account, is proposed in this paper. First, DEM-ICP filters the point clouds to remove the non-ground points. Second, the ground points are interpolated to generate continuous DEMs. Finally, a point-to-plane ICP algorithm is performed to register the adjacent DEMs with the overlapping area. A graph-based optimization is utilized following DEM-ICP to estimate the correction parameters and achieve global consistency between all strips. Experiments were carried out using eight strips collected by ULS in mountainous areas to evaluate the proposed method. The average root-mean-square error (RMSE) of all data was less than 0.4 m after the proposed strip adjustment, which was only 0.015 m higher than the result of manual registration (ground truth). In addition, the plane fitting accuracy of lateral point clouds was improved 4.2-fold, from 1.565 to 0.375 m, demonstrating the robustness and accuracy of the proposed method. Full article

(This article belongs to the Special Issue Selected Papers from The Sixth National LiDAR Conference)

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18 pages, 10596 KiB

Open AccessArticle

Robust Coarse-to-Fine Registration Scheme for Mobile Laser Scanner Point Clouds Using Multiscale Eigenvalue Statistic-Based Descriptor

by Yongjian Fu, Zongchun Li, Wenqi Wang, Hua He, Feng Xiong and Yong Deng

Sensors 2021, 21(7), 2431; https://doi.org/10.3390/s21072431 - 1 Apr 2021

Cited by 5 | Viewed by 2345

Abstract

To overcome the drawbacks of pairwise registration for mobile laser scanner (MLS) point clouds, such as difficulty in searching the corresponding points and inaccuracy registration matrix, a robust coarse-to-fine registration method is proposed to align different frames of MLS point clouds into a common coordinate system. The method identifies the correct corresponding point pairs from the source and target point clouds, and then calculates the transform matrix. First, the performance of a multiscale eigenvalue statistic-based descriptor with different combinations of parameters is evaluated to identify the optimal combination. Second, based on the geometric distribution of points in the neighborhood of the keypoint, a weighted covariance matrix is constructed, by which the multiscale eigenvalues are calculated as the feature description language. Third, the corresponding points between the source and target point clouds are estimated in the feature space, and the incorrect ones are eliminated via a geometric consistency constraint. Finally, the estimated corresponding point pairs are used for coarse registration. The value of coarse registration is regarded as the initial value for the iterative closest point algorithm. Subsequently, the final fine registration result is obtained. The results of the registration experiments with Autonomous Systems Lab (ASL) Datasets show that the proposed method can accurately align MLS point clouds in different frames and outperform the comparative methods. Full article

(This article belongs to the Special Issue Selected Papers from The Sixth National LiDAR Conference)

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20 pages, 1370 KiB

Open AccessArticle

Point Cloud Semantic Segmentation Network Based on Multi-Scale Feature Fusion

by Jing Du, Zuning Jiang, Shangfeng Huang, Zongyue Wang, Jinhe Su, Songjian Su, Yundong Wu and Guorong Cai

Sensors 2021, 21(5), 1625; https://doi.org/10.3390/s21051625 - 26 Feb 2021

Cited by 16 | Viewed by 5133

Abstract

The semantic segmentation of small objects in point clouds is currently one of the most demanding tasks in photogrammetry and remote sensing applications. Multi-resolution feature extraction and fusion can significantly enhance the ability of object classification and segmentation, so it is widely used in the image field. For this motivation, we propose a point cloud semantic segmentation network based on multi-scale feature fusion (MSSCN) to aggregate the feature of a point cloud with different densities and improve the performance of semantic segmentation. In our method, random downsampling is first applied to obtain point clouds of different densities. A Spatial Aggregation Net (SAN) is then employed as the backbone network to extract local features from these point clouds, followed by concatenation of the extracted feature descriptors at different scales. Finally, a loss function is used to combine the different semantic information from point clouds of different densities for network optimization. Experiments were conducted on the S3DIS and ScanNet datasets, and our MSSCN achieved accuracies of 89.80% and 86.3%, respectively, on these datasets. Our method showed better performance than the recent methods PointNet, PointNet++, PointCNN, PointSIFT, and SAN. Full article

(This article belongs to the Special Issue Selected Papers from The Sixth National LiDAR Conference)

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15 pages, 6191 KiB

Open AccessArticle

ALS Point Cloud Classification by Integrating an Improved Fully Convolutional Network into Transfer Learning with Multi-Scale and Multi-View Deep Features

by Xiangda Lei, Hongtao Wang, Cheng Wang, Zongze Zhao, Jianqi Miao and Puguang Tian

Sensors 2020, 20(23), 6969; https://doi.org/10.3390/s20236969 - 6 Dec 2020

Cited by 10 | Viewed by 3570

Abstract

Airborne laser scanning (ALS) point cloud has been widely used in various fields, for it can acquire three-dimensional data with a high accuracy on a large scale. However, due to the fact that ALS data are discretely, irregularly distributed and contain noise, it is still a challenge to accurately identify various typical surface objects from 3D point cloud. In recent years, many researchers proved better results in classifying 3D point cloud by using different deep learning methods. However, most of these methods require a large number of training samples and cannot be widely used in complex scenarios. In this paper, we propose an ALS point cloud classification method to integrate an improved fully convolutional network into transfer learning with multi-scale and multi-view deep features. First, the shallow features of the airborne laser scanning point cloud such as height, intensity and change of curvature are extracted to generate feature maps by multi-scale voxel and multi-view projection. Second, these feature maps are fed into the pre-trained DenseNet201 model to derive deep features, which are used as input for a fully convolutional neural network with convolutional and pooling layers. By using this network, the local and global features are integrated to classify the ALS point cloud. Finally, a graph-cuts algorithm considering context information is used to refine the classification results. We tested our method on the semantic 3D labeling dataset of the International Society for Photogrammetry and Remote Sensing (ISPRS). Experimental results show that overall accuracy and the average F1 score obtained by the proposed method is 89.84% and 83.62%, respectively, when only 16,000 points of the original data are used for training. Full article

(This article belongs to the Special Issue Selected Papers from The Sixth National LiDAR Conference)

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