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Hyperspectral Imaging and LiDAR Scanning Technology Development and Applications

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 1354

Special Issue Editors


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Guest Editor
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Vuorimiehentie 5, FI-02150 Espoo, Finland
Interests: hyperspectral imaging technology; hyperspectral LiDAR; infrared imaging; machine learning
Special Issues, Collections and Topics in MDPI journals
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: geospatial data analysis; LiDAR cloud data processing; urban informatics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Communication and Computer Network Lab of Guangdong, School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China
Interests: hyperspectral image processing; artificial intelligence; semi-supervised learning

Special Issue Information

Dear Colleagues,

As the main technical approach of Earth observation, remote sensing has been widely used in ecology, agronomy, forestry, geography, and environmental science. The main remote sensing techniques include active information acquisition methods (e.g., synthetic aperture radar (SAR) and light detection and ranging (LiDAR)) and passive optical imaging approaches (e.g., high-resolution imagery and hyperspectral imagery). LiDAR can obtain the range and 3D spatial information of the target and is not easily affected by environmental factors such as changes in illumination conditions or weather. However, it cannot obtain spectral data, and current airborne LiDAR systems have fewer than three bands. Hyperspectral images have many channels (generally more than 100) and continuous spectrum coverage and have been used for identification and classification in many fields. However, the range information of the target cannot be obtained, and it is easily affected by obstructions such as clouds or forest canopies. Hyperspectral LiDAR is a new technology that has emerged in recent years. It combines the advantages of LiDAR and hyperspectral images but still requires more effort in large-scale detector technology, data processing, and application exploration. This Special Issue of Remote Sensing aims to provide a platform for researchers to publish innovative work on advances, methods, and applications of hyperspectral imaging and LiDAR scanning techniques. Potential research will include but not be limited to:

  • Design, calibration, and performance evaluation of hyperspectral imaging sensors;
  • Development and applications of LiDAR systems;
  • Hyperspectral LiDAR technology;
  • Data fusion of hyperspectral images and point clouds;
  • Development of artificial intelligence algorithms for remote sensing data;
  • Application exploration of hyperspectral imaging and LiDAR techniques.

Dr. Jianxin Jia
Dr. Yuwei Chen
Dr. Yue Yu
Dr. Xiaorou Zheng
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

  • hyperspectral imaging
  • LiDAR
  • data processing
  • artificial intelligence
  • remote sensing applications

Published Papers (2 papers)

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18 pages, 2133 KiB  
Article
Analysis of Road Surface Texture for Asphalt Pavement Adhesion Assessment Using 3D Laser Technology
by Haimei Liang, Rosa Giovanna Pagano, Stefano Oddone, Lin Cong and Maria Rosaria De Blasiis
Remote Sens. 2024, 16(11), 1943; https://doi.org/10.3390/rs16111943 (registering DOI) - 28 May 2024
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Abstract
Pavement adhesion plays a crucial role in driving safety, while traditional test methods exhibit some limitations. To improve the efficiency and accuracy of asphalt pavement texture characterization and adhesion assessments, this paper uses three-dimensional (3D) laser technology to detect the continuous point cloud [...] Read more.
Pavement adhesion plays a crucial role in driving safety, while traditional test methods exhibit some limitations. To improve the efficiency and accuracy of asphalt pavement texture characterization and adhesion assessments, this paper uses three-dimensional (3D) laser technology to detect the continuous point cloud data of road surface and reconstruct the 3D topography of pavement texture. On this basis, a volume parameter Volume of peak materials (Vmp) is innovatively proposed to comprehensively characterize the 3D spatial characteristics of road surface texture. The correlation analysis between the proposed Vmp and the traditional adhesion evaluation index Transversal Adhesion Coefficient (CAT) is conducted, and then refined graded adhesion prediction models based on the proposed Vmp are proposed. Results show that the proposed volume parameter Vmp can reliably and accurately characterize the asphalt pavement texture by considering more structural properties of the road surface texture. According to the research findings of this paper, it is feasible to achieve rapid and correct assessment of asphalt pavement adhesion using 3D laser detection technology by comprehensively considering the 3D characteristics of the road surface texture. Full article

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13 pages, 7949 KiB  
Technical Note
Speckle Noise Reduction via Linewidth Broadening for Planetary Laser Reflectance Spectrometers
by Daniel R. Cremons, Gregory B. Clarke and Xiaoli Sun
Remote Sens. 2024, 16(9), 1515; https://doi.org/10.3390/rs16091515 - 25 Apr 2024
Viewed by 361
Abstract
The low obliquity of the Moon leads to challenging solar illumination conditions at the poles, especially for passive reflectance measurements aimed at determining the presence and extent of surface volatiles. A nascent alternate method is to use active laser illumination sources in either [...] Read more.
The low obliquity of the Moon leads to challenging solar illumination conditions at the poles, especially for passive reflectance measurements aimed at determining the presence and extent of surface volatiles. A nascent alternate method is to use active laser illumination sources in either a multispectral or hyperspectral design. With a laser spectral source, however, the achievable reflectance precision may be limited by speckle noise resulting from the interference effects of a coherent beam interacting with a rough surface. Here, we have experimentally tested the use of laser linewidth broadening to reduce speckle noise and, thus, increase reflectance precision. We performed a series of speckle imaging tests with near-infrared laser sources of varying coherence, compared them to both theory and speckle pattern simulations, and measured the reflectance precision using calibrated targets. By increasing the laser linewidth, we observed a reduction in speckle contrast and the corresponding increase in reflectance precision, which was 80% of the theoretical improvement. Finally, we discuss methods of laser linewidth broadening and spectral resolution requirements for planetary laser reflectance spectrometers. Full article
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