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Remote Sensing
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Advancing UAV-Based Remote Sensing: Innovations, Techniques and Applications
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Advancing UAV-Based Remote Sensing: Innovations, Techniques 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: 15 November 2025 | Viewed by 1347

Special Issue Editors

Dr. Rui Li

E-Mail Website
Guest Editor
School of Engineering, University of Warwick, Coventry, UK
Interests: UAV and remote sensing image processing; computer vision; offshore wind energy
Special Issues, Collections and Topics in MDPI journals
Dr. Yinxia Cao

E-Mail Website
Guest Editor
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Interests: building height; weakly supervised learning; multi-view imagery; high resolution; change detection
Special Issues, Collections and Topics in MDPI journals
Dr. Haoyang Yang

E-Mail
Guest Editor
Faculty of Science, Engineering and Medicine, University of Warwick, Coventry, UK
Interests: UAV applications; machine learning; planning and navigation
Special Issues, Collections and Topics in MDPI journals
Dr. Dongyu Li

E-Mail Website
Guest Editor
School of Cyber Science and Technolgoy, Beihang University, Beijing 100191, China
Interests: UAV intelligent and application
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Unmanned aerial vehicles (UAVs) have revolutionized remote sensing, offering unprecedented opportunities for high-resolution, flexible, and cost-effective data collection. With advancements in UAV technology, sensors, and data processing algorithms, these systems are now have various critical environmental, agricultural, and urban applications. From efficient flight path planning to innovative image enhancement techniques, UAV-based remote sensing continues to expand the boundaries of research and acquire even more practical applications.

This Special Issue aims to showcase cutting-edge research and practical developments in the field of UAV-based remote sensing. We welcome studies addressing the full spectrum of UAV operations, including flight planning optimization, data acquisition, advanced image processing, and the diverse applications of UAVs in environmental monitoring, disaster response, and precision agriculture. Contributions exploring the integration of multispectral, hyperspectral, and thermal imaging, as well as innovative uses of machine learning and artificial intelligence in UAV applications, are highly encouraged.

Potential topics for this Special Issue include, but are not limited to, the following:

  • UAV path planning and autonomous navigation for remote sensing;
  • Image enhancement techniques, including denoising and super-resolution;
  • Multispectral and hyperspectral UAV applications;
  • Thermal imaging and its applications in environmental and industrial studies;
  • Precision agriculture using UAVs;
  • The use of UAVs for disaster monitoring and management;
  • Forest and vegetation monitoring;
  • Data fusion techniques for the integration of UAVs and satellites;
  • Innovations in UAV sensor design and payload optimization;
  • Ethical and regulatory considerations in UAV remote sensing.

We invite researchers and practitioners to contribute original research articles, reviews, and technical notes that will advance the art of UAV-based remote sensing.

You may choose our Joint Special Issue in Drones.

Dr. Rui Li
Dr. Yinxia Cao
Dr. Haoyang Yang
Dr. Dongyu Li
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

  • UAV remote sensing
  • drone path planning
  • image enhancement
  • multispectral and hyperspectral imaging
  • thermal remote sensing
  • precision mapping
  • data fusion techniques
  • environmental monitoring
  • disaster response applications
  • machine learning in UAV data analysis

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

Published Papers (3 papers)

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Research

19 pages, 3265 KiB  
Article
Indeterminacy of Camera Intrinsic Parameters in Structure from Motion Using Images from Constant-Pitch Flight Design
by Truc Thanh Ho, Riku Sato, Ariyo Kanno, Tsuyoshi Imai, Koichi Yamamoto and Takaya Higuchi
Remote Sens. 2025, 17(12), 2030; https://doi.org/10.3390/rs17122030 - 12 Jun 2025
Abstract
Intrinsic parameter estimation by self-calibration is commonly used in Unmanned aerial vehicle (UAV)-based photogrammetry with Structure from Motion (SfM). However, obtaining stable estimates of these parameters from image-based SfM—which relies solely on images, without auxiliary data such as ground control points (GCPs)—remains challenging. [...] Read more.
Intrinsic parameter estimation by self-calibration is commonly used in Unmanned aerial vehicle (UAV)-based photogrammetry with Structure from Motion (SfM). However, obtaining stable estimates of these parameters from image-based SfM—which relies solely on images, without auxiliary data such as ground control points (GCPs)—remains challenging. Aerial imagery acquired with the constant-pitch (CP) flight pattern often exhibits non-linear deformations, highly unstable intrinsic parameters, and even alignment failures. We hypothesize that CP flights form a “critical configuration” that renders certain intrinsic parameters indeterminate. Through numerical experiments, we confirm that a CP flight configuration does not provide sufficient constraints to estimate focal length (f) and the principal point coordinate (cy) in image-based SfM. Real-world CP datasets further demonstrate the pronounced instability of these parameters. As a remedy, we show that by introducing intermediate strips into the CP flight plan—what we call a CP-Plus flight—can effectively mitigate the indeterminacy of f and cy in simulations and markedly improve their stability in all tested cases. This approach enables more effective image-only SfM workflows without auxiliary data, simplifies data acquisition, and improves three-dimensional reconstruction accuracy. Full article
(This article belongs to the Special Issue Advancing UAV-Based Remote Sensing: Innovations, Techniques and Applications)
21 pages, 14200 KiB  
Article
A Re-Identification Framework for Visible and Thermal-Infrared Aerial Remote Sensing Images with Large Differences of Elevation Angles
by Chunhui Zhao, Wenxuan Wang, Yiming Yan, Baoyu Ge, Wei Hou and Fengjiao Gao
Remote Sens. 2025, 17(11), 1956; https://doi.org/10.3390/rs17111956 - 5 Jun 2025
Viewed by 188
Abstract
Visible and thermal-infrared re-identification (VTI-ReID) based on aerial images is a challenging task due to the large range of elevation angles, which exacerbates the modality differences between different modalities. The substantial modality gap makes it challenging for existing methods to extract identity information [...] Read more.
Visible and thermal-infrared re-identification (VTI-ReID) based on aerial images is a challenging task due to the large range of elevation angles, which exacerbates the modality differences between different modalities. The substantial modality gap makes it challenging for existing methods to extract identity information from aerial images captured at wide elevation angles. This limitation significantly reduces VTI-ReID accuracy. This issue is particularly pronounced in elongated targets. To address this issue, a robust framework for extracting identity representation (RIRE) is proposed, specifically designed for VTI-ReID in aerial cross-modality images. This framework adopts a mapping method based on global representation decomposition and local representation aggregation. It effectively extracts features related to identity from aerial images and aligns the global representations of images captured from different angles within the same identity space. This approach enhances the adaptability of the VTI-ReID task to elevation angle differences. To validate the effectiveness of the proposed framework, a dataset group for elongated target VTI-ReID based on unmanned aerial vehicle (UAV)-captured data has been created. Extensive evaluations of the proposed framework on the proposed dataset group indicate that the framework significantly improves the robustness of the extracted identity information for elongated targets in aerial images, thereby enhancing the accuracy of VTI-ReID. Full article
(This article belongs to the Special Issue Advancing UAV-Based Remote Sensing: Innovations, Techniques and Applications)
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32 pages, 99149 KiB  
Article
Optimizing Camera Settings and Unmanned Aerial Vehicle Flight Methods for Imagery-Based 3D Reconstruction: Applications in Outcrop and Underground Rock Faces
by Junsu Leem, Seyedahmad Mehrishal, Il-Seok Kang, Dong-Ho Yoon, Yulong Shao, Jae-Joon Song and Jinha Jung
Remote Sens. 2025, 17(11), 1877; https://doi.org/10.3390/rs17111877 - 28 May 2025
Viewed by 231
Abstract
The structure from motion (SfM) and multiview stereo (MVS) techniques have proven effective in generating high-quality 3D point clouds, particularly when integrated with unmanned aerial vehicles (UAVs). However, the impact of image quality—a critical factor for SfM–MVS techniques—has received limited attention. This study [...] Read more.
The structure from motion (SfM) and multiview stereo (MVS) techniques have proven effective in generating high-quality 3D point clouds, particularly when integrated with unmanned aerial vehicles (UAVs). However, the impact of image quality—a critical factor for SfM–MVS techniques—has received limited attention. This study proposes a method for optimizing camera settings and UAV flight methods to minimize point cloud errors under illumination and time constraints. The effectiveness of the optimized settings was validated by comparing point clouds generated under these conditions with those obtained using arbitrary settings. The evaluation involved measuring point-to-point error levels for an indoor target and analyzing the standard deviation of cloud-to-mesh (C2M) and multiscale model-to-model cloud comparison (M3C2) distances across six joint planes of a rock mass outcrop in Seoul, Republic of Korea. The results showed that optimal settings improved accuracy without requiring additional lighting or extended survey time. Furthermore, we assessed the performance of SfM–MVS under optimized settings in an underground tunnel in Yeoju-si, Republic of Korea, comparing the resulting 3D models with those generated using Light Detection and Ranging (LiDAR). Despite challenging lighting conditions and time constraints, the results suggest that SfM–MVS with optimized settings has the potential to produce 3D models with higher accuracy and resolution at a lower cost than LiDAR in such environments. Full article
(This article belongs to the Special Issue Advancing UAV-Based Remote Sensing: Innovations, Techniques and Applications)
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