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Remote Sensing
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Land Cover Classification Using Multispectral LiDAR Data
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Land Cover Classification Using Multispectral LiDAR Data

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 17400

Special Issue Editors

Prof. Dr. Wei Gong

E-Mail Website
Guest Editor
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Interests: LiDAR hardware; advanced sensors; haze events; climate change
Special Issues, Collections and Topics in MDPI journals
Dr. Shalei Song

E-Mail Website
Guest Editor
Innovation Academy of Precision Measurement Science and Technology, Chinese Academy of sciences, Wuhan 430071, China
Interests: multispectral/hyperspectral LiDAR hardware system design; full-waveform data processing; multispectral LiDAR point clouds modeling and colorful visualization; ground object classification based on multispectral/hyperspectral point clouds
Prof. Dr. Shuo Shi

E-Mail Website
Guest Editor
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Interests: multispectral/hyperspectral LiDAR; LiDAR hardware; advanced laser sensors; processing of LiDAR point cloud data; vegetation remote sensing based on Lidar; correction of LiDAR intensity; laser-induced fluorescence
Prof. Dr. Haiyan Guan

E-Mail Website
Guest Editor
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: airborne/mobile laser scanning data processing; remote sensing image data understanding; multispectral/hyperspectral point clouds for semantic interpretation of wetlands; cultivated and vegetated areas
Special Issues, Collections and Topics in MDPI journals
Dr. Yongtao Yu

E-Mail Website
Guest Editor
Huaiyin Institute of Technology, Huai'an 223003, China
Interests: point cloud data processing; remote sensing image processing; object detection; object segmentation; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

 As an active and accurate remote sensing technology, multispectral LiDAR integrates 3D geometric and spectral information and has been actively applied in many applications, ranging from land use/land cover classification, 3D urban modeling, and road inspections to forest inventory. Multispectral LiDAR data can be acquired not only by the fusion of passive image data, such as from multispectral images, but also through increasing the receiving channels for terrestrial, mobile, or airborne laser scanning (TLS/MLS/ALS) based on wavelengths of interest.

However, multispectral LiDAR data obtained from these systems have the unique features of multidimensional attributes, scene complexity, and data incompleteness. It remains a challenge to achieve efficient and effective land cover classification that includes data fusion, wavelength selection, waveform data processing, feature extraction, target detection, and semantic labeling, segmentation, and classification in addition to large-scale point clouds for 3D scene modeling, geospatial mapping, and environmental monitoring applications. We are pleased to announce a call for papers on land cover classification using multispectral LiDAR data obtained from different platforms.

This Special Issue welcomes contributions that showcase the recent advancements in land cover classification using multispectral LiDAR data to support environmental monitoring, geospatial big data analysis, and 3D modeling.

Prof. Dr. Wei Gong
Dr. Shalei Song
Dr. Shuo Shi
Prof. Dr. Haiyan Guan
Dr. Yongtao Yu
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. Remote Sensing 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 2700 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

  • multispectral LiDAR
  • hyperspectral LiDAR
  • land cover classification
  • target extraction
  • machine/deep learning
  • feature engineering
  • semantic interpretation of wetlands and cultivated and vegetated areas

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Published Papers (4 papers)

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22 pages, 14811 KiB  
Article
Target Classification of Similar Spatial Characteristics in Complex Urban Areas by Using Multispectral LiDAR
by Binhan Luo, Jian Yang, Shalei Song, Shuo Shi, Wei Gong, Ao Wang and Lin Du
Remote Sens. 2022, 14(1), 238; https://doi.org/10.3390/rs14010238 - 5 Jan 2022
Cited by 37 | Viewed by 4576
Abstract
With the rapid modernization, many remote-sensing sensors were developed for classifying urban land and environmental monitoring. Multispectral LiDAR, which serves as a new technology, has exhibited potential in remote-sensing monitoring due to the synchronous acquisition of three-dimension point cloud and spectral information. This [...] Read more.
With the rapid modernization, many remote-sensing sensors were developed for classifying urban land and environmental monitoring. Multispectral LiDAR, which serves as a new technology, has exhibited potential in remote-sensing monitoring due to the synchronous acquisition of three-dimension point cloud and spectral information. This study confirmed the potential of multispectral LiDAR for complex urban land cover classification through three comparative methods. Firstly, the Optech Titan LiDAR point cloud was pre-processed and ground filtered. Then, three methods were analyzed: (1) Channel 1, based on Titan data to simulate the classification of a single-band LiDAR; (2) three-channel information and the digital surface model (DSM); and (3) three-channel information and DSM combined with the calculated three normalized difference vegetation indices (NDVIs) for urban land classification. A decision tree was subsequently used in classification based on the combination of intensity information, elevation information, and spectral information. The overall classification accuracies of the point cloud using the single-channel classification and the multispectral LiDAR were 64.66% and 93.82%, respectively. The results show that multispectral LiDAR has excellent potential for classifying land use in complex urban areas due to the availability of spectral information and that the addition of elevation information to the classification process could boost classification accuracy. Full article
(This article belongs to the Special Issue Land Cover Classification Using Multispectral LiDAR Data)
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16 pages, 8403 KiB  
Article
Parameter Simulation and Design of an Airborne Hyperspectral Imaging LiDAR System
by Liyong Qian, Decheng Wu, Dong Liu, Shalei Song, Shuo Shi, Wei Gong and Le Wang
Remote Sens. 2021, 13(24), 5123; https://doi.org/10.3390/rs13245123 - 17 Dec 2021
Cited by 5 | Viewed by 2755
Abstract
With continuous technological development, the future development trend of LiDAR in the field of remote sensing and mapping is to obtain the elevation and spectral information of ground targets simultaneously. Airborne hyperspectral imaging LiDAR inherits the advantages of active and passive remote sensing [...] Read more.
With continuous technological development, the future development trend of LiDAR in the field of remote sensing and mapping is to obtain the elevation and spectral information of ground targets simultaneously. Airborne hyperspectral imaging LiDAR inherits the advantages of active and passive remote sensing detection. This paper presents a simulation method to determine the design parameters of an airborne hyperspectral imaging LiDAR system. In accordance with the hyperspectral imaging LiDAR equation and optical design principles, the atmospheric transmission model and the reflectance spectrum of specific ground targets are utilized. The design parameters and laser emission spectrum of the hyperspectral LiDAR system are considered, and the signal-to-noise ratio of the system is obtained through simulation. Without considering the effect of detector gain and electronic amplification on the signal-to-noise ratio, three optical fibers are coupled into a detection channel, and the power spectral density emitted by the supercontinuum laser is simulated by assuming that the signal-to-noise ratio is equal to 1. The power spectral density emitted by the laser must not be less than 15 mW/nm in the shortwave direction. During the simulation process, the design parameters of the hyperspectral LiDAR system are preliminarily demonstrated, and the feasibility of the hyperspectral imaging LiDAR system design is theoretically guaranteed in combination with the design requirements of the supercontinuum laser. The spectral resolution of a single optical fiber of the hyperspectral LiDAR system is set to 2.5 nm. In the actual prototype system, multiple optical fibers can be coupled into a detection channel in accordance with application needs to further improve the signal-to-noise ratio of hyperspectral LiDAR system detection. Full article
(This article belongs to the Special Issue Land Cover Classification Using Multispectral LiDAR Data)
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21 pages, 5536 KiB  
Article
Land Cover Classification with Multispectral LiDAR Based on Multi-Scale Spatial and Spectral Feature Selection
by Shuo Shi, Sifu Bi, Wei Gong, Biwu Chen, Bowen Chen, Xingtao Tang, Fangfang Qu and Shalei Song
Remote Sens. 2021, 13(20), 4118; https://doi.org/10.3390/rs13204118 - 14 Oct 2021
Cited by 21 | Viewed by 2772
Abstract
The distribution of land cover has an important impact on climate, environment, and public policy planning. The Optech Titan multispectral LiDAR system provides new opportunities and challenges for land cover classification, but the better application of spectral and spatial information of multispectral LiDAR [...] Read more.
The distribution of land cover has an important impact on climate, environment, and public policy planning. The Optech Titan multispectral LiDAR system provides new opportunities and challenges for land cover classification, but the better application of spectral and spatial information of multispectral LiDAR data is a problem to be solved. Therefore, we propose a land cover classification method based on multi-scale spatial and spectral feature selection. The public data set of Tobermory Port collected by the Optech Titan multispectral airborne laser scanner was used as research data, and the data was manually divided into eight categories. The method flow is divided into four steps: neighborhood point selection, spatial–spectral feature extraction, feature selection, and classification. First, the K-nearest neighborhood is used to select the neighborhood points for the multispectral LiDAR point cloud data. Additionally, the spatial and spectral features under the multi-scale neighborhood (K = 20, 50, 100, 150) are extracted. The Equalizer Optimization algorithm is used to perform feature selection on multi-scale neighborhood spatial–spectral features, and a feature subset is obtained. Finally, the feature subset is input into the support vector machine (SVM) classifier for training. Using only small training samples (about 0.5% of the total data) to train the SVM classifier, 91.99% overall accuracy (OA), 93.41% average accuracy (AA) and 0.89 kappa coefficient were obtained in study area. Compared with the original information’s classification result, the OA, AA and kappa coefficient increased by 15.66%, 8.7% and 0.19, respectively. The results show that the constructed spatial–spectral features and the application of the Equalizer Optimization algorithm for feature selection are effective in land cover classification with Titan multispectral LiDAR point data. Full article
(This article belongs to the Special Issue Land Cover Classification Using Multispectral LiDAR Data)
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19 pages, 6759 KiB  
Article
Multispectral LiDAR Point Cloud Classification Using SE-PointNet++
by Zhuangwei Jing, Haiyan Guan, Peiran Zhao, Dilong Li, Yongtao Yu, Yufu Zang, Hanyun Wang and Jonathan Li
Remote Sens. 2021, 13(13), 2516; https://doi.org/10.3390/rs13132516 - 27 Jun 2021
Cited by 54 | Viewed by 5906
Abstract
A multispectral light detection and ranging (LiDAR) system, which simultaneously collects spatial geometric data and multi-wavelength intensity information, opens the door to three-dimensional (3-D) point cloud classification and object recognition. Because of the irregular distribution property of point clouds and the massive data [...] Read more.
A multispectral light detection and ranging (LiDAR) system, which simultaneously collects spatial geometric data and multi-wavelength intensity information, opens the door to three-dimensional (3-D) point cloud classification and object recognition. Because of the irregular distribution property of point clouds and the massive data volume, point cloud classification directly from multispectral LiDAR data is still challengeable and questionable. In this paper, a point-wise multispectral LiDAR point cloud classification architecture termed as SE-PointNet++ is proposed via integrating a Squeeze-and-Excitation (SE) block with an improved PointNet++ semantic segmentation network. PointNet++ extracts local features from unevenly sampled points and represents local geometrical relationships among the points through multi-scale grouping. The SE block is embedded into PointNet++ to strengthen important channels to increase feature saliency for better point cloud classification. Our SE-PointNet++ architecture has been evaluated on the Titan multispectral LiDAR test datasets and achieved an overall accuracy, a mean Intersection over Union (mIoU), an F1-score, and a Kappa coefficient of 91.16%, 60.15%, 73.14%, and 0.86, respectively. Comparative studies with five established deep learning models confirmed that our proposed SE-PointNet++ achieves promising performance in multispectral LiDAR point cloud classification tasks. Full article
(This article belongs to the Special Issue Land Cover Classification Using Multispectral LiDAR Data)
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