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Remote Sensing
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New Advances in Laser Remote Sensing in China
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New Advances in Laser Remote Sensing in China

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 4490

Special Issue Editors

Res. Prof. Dr. Yi Lin

E-Mail Website
Guest Editor
Institute of Remote Sensing and GIS, School of Earth and Space Sciences, Peking University, Beijing, China
Interests: beyond-3D LiDAR remote sensing; 3D forest ecology; circumpolar macro-ecosystem ecology; cube Earth interactions; 3D global ecology
Dr. Craig Glennie

E-Mail Website
Guest Editor
Civil & Environmental Engineering, University of Houston, Houston, TX 77004, USA
Interests: kinematic remote sensing system integration and calibration; LiDAR processing and analysis; 3D change detection; open source software development
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, the field of laser remote sensing has rapidly expanded in China, and has seen the emergence of new principles and technologies. To capture, consolidate, promote, and propagate the very latest creative ideas and practices around the new theories, mechanism exploration, methodology development, and application extension in China, this Special Issue attempts to document these revolutionary developments and innovative applications.

Well-prepared, unpublished papers that address interesting topics involving the biosphere, atmosphere, hydrosphere, and geosphere, with use of laser remote sensing data from any source (e.g., satellite, airborne, drones, and field) are cordially solicited. Note that this Special Issue will go along with the Chinese Graduate Conference on Laser Remote Sensing Frontiers scheduled for December, 2019, and thus, the first author of any submitted paper for consideration must be a graduate student studying in China.

Res. Prof. Dr. Yi Lin
Assoc. Prof. Craig Glennie
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

  • Laser remote sensing
  • Light detection and ranging (LiDAR)
  • Laser inspection
  • Laser mapping
  • Theory proposal
  • Mechanism exploration
  • Methodology development
  • Application extension
  • Biosphere
  • Atmosphere
  • Hydrosphere
  • Geosphere
  • Space

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

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Research

17 pages, 8639 KiB  
Article
True-Color Three-Dimensional Imaging and Target Classification BASED on Hyperspectral LiDAR
by Bowen Chen, Shuo Shi, Wei Gong, Jia Sun, Biwu Chen, Lin Du, Jian Yang, Kuanghui Guo and Xingmin Zhao
Remote Sens. 2019, 11(13), 1541; https://doi.org/10.3390/rs11131541 - 28 Jun 2019
Cited by 21 | Viewed by 4062
Abstract
True-color three-dimensional (3D) imaging exploits spatial and spectral information and can enable accurate feature extraction and object classification. The existing methods, however, are limited by data collection mechanisms when realizing true-color 3D imaging. We overcome this problem and present a novel true-color 3D [...] Read more.
True-color three-dimensional (3D) imaging exploits spatial and spectral information and can enable accurate feature extraction and object classification. The existing methods, however, are limited by data collection mechanisms when realizing true-color 3D imaging. We overcome this problem and present a novel true-color 3D imaging method based on a 32-channel hyperspectral LiDAR (HSL) covering a 431–751 nm spectral range. We conducted two experiments, one with nine-color card papers and the other with seven different colored objects. We used the former to investigate the effect of true-color 3D imaging and determine the optimal spectral bands for compositing true-color, and the latter to explore the classification potential based on the true-color feature using polynomial support vector machine (SVM) and Gaussian naive Bayes (NB) classifiers. Since using all bands of HSL will cause color distortions, the optimal spectral band combination for better compositing the true-color were selected by principal component analysis (PCA) and spectral correlation measure (SCM); PCA emphasizes the amount of information in band combinations, while SCM focuses on correlation between bands. The results show that the true-color 3D imaging can be realized based on HSL measurements, and three spectral bands of 466, 546, and 626 nm were determined. Comparing reflectance of the three selected bands, the overall classification accuracy of seven different colored objects was improved by 14.6% and 8.25% based on SVM and NB, respectively, classifiers after converting spectral intensities into true-color information. Overall, this study demonstrated the potential of HSL system in retrieving true-color and facilitating target recognition, and can serve as a guide in developing future three-channel or multi-channel true-color LiDAR. Full article
(This article belongs to the Special Issue New Advances in Laser Remote Sensing in China)
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