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Advancing UAV-Based Remote Sensing: Innovations, Techniques and Applications

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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 (15 November 2025) | Viewed by 12822

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Dr. Rui Li

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School of Engineering, University of Warwick, Coventry CV4 7AL, UK
Interests: UAV and remote sensing image processing; computer vision; offshore wind energy
Special Issues, Collections and Topics in MDPI journals

Dr. Yinxia Cao

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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
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Dr. Haoyang Yang

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Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong
Interests: UAV application; machine learning, planning and navigation
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Dr. Dongyu Li

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Guest Editor

School of Cyber Science and Technolgoy, Beihang University, Beijing 100191, China
Interests: UAV intelligent and application
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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 250 words) can be sent to the Editorial Office for assessment.

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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Related Special Issue

Advancing UAV-Based Remote Sensing: Innovations, Techniques and Applications (Second Edition) in Remote Sensing

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Research

19 pages, 17292 KB

Open AccessArticle

Optimization of Spatial Sampling in Satellite–UAV Integrated Remote Sensing: Rationale and Applications in Crop Monitoring

by Zhen Zhao, Hang Xiong, Yawen Yu, Baodong Xu and Jian Zhang

Remote Sens. 2025, 17(23), 3895; https://doi.org/10.3390/rs17233895 - 30 Nov 2025

Viewed by 461

Abstract

Satellite and UAV-based remote sensing have been widely used for agricultural systems monitoring jointly. How to quantitatively optimize the efficiency of integrating these two techniques remains largely understudied. To address this gap, we, for the first time, formulate the configuration of satellite–UAV integrated system as a spatial sampling optimization problem and propose an SSO (spatial sampling optimization) model that jointly optimizes the spatial locations and flight paths of UAV sampling within the satellite monitoring area. The SSO model enables maximizing the accuracy of monitoring under a given cost constraint. We obtained comprehensive data in rapeseed fields and conducted experiments based on the SSO model. We compared the sampling effectiveness of the SSO model with that of simple random sampling, systematic sampling, equal stratified sampling and Neyman stratified sampling. The results showed that the SSO-optimized plan had the highest sampling efficiency, which was at least 38.7% higher than that of the best-performing conventional method (Neyman stratified sampling). Under the same cost constraint, the SSO-optimized sampling scheme can have 11.1% more sampling points than the conventional sampling scheme. The Elite Genetic Algorithm (EGA) performed well in solving the SSO model. The error of the SSO-optimized scheme was reduced by 27.3% and the sampling distance was reduced by 7000 to 8000 m on average. In conclusion, the proposed SSO model helps to optimize the configuration of satellite–UAV integrated remote sensing, thereby improving the cost-effectiveness of agricultural monitoring systems. We call for considering cost constraints and increasing efficiency in agricultural system monitoring and government censuses in the future. Full article

(This article belongs to the Special Issue Advancing UAV-Based Remote Sensing: Innovations, Techniques and Applications)

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21 pages, 9487 KB

Open AccessArticle

Low-Cost Real-Time Remote Sensing and Geolocation of Moving Targets via Monocular Bearing-Only Micro UAVs

by Peng Sun, Shiji Tong, Kaiyu Qin, Zhenbing Luo, Boxian Lin and Mengji Shi

Remote Sens. 2025, 17(23), 3836; https://doi.org/10.3390/rs17233836 - 27 Nov 2025

Viewed by 382

Abstract

Low-cost and real-time remote sensing of moving targets is increasingly required in civilian applications. Micro unmanned aerial vehicles (UAVs) provide a promising platform for such missions because of their small size and flexible deployment, but they are constrained by payload capacity and energy budget. Consequently, they typically carry lightweight monocular cameras only. These cameras cannot directly measure distance and suffer from scale ambiguity, which makes accurate geolocation difficult. This paper tackles geolocation and short-term trajectory prediction of moving targets over uneven terrain using bearing-only measurements from a monocular camera. We present a two-stage estimation framework in which a pseudo-linear Kalman filter (PLKF) provides real-time state estimates, while a sliding-window nonlinear least-squares (NLS) back end refines them. Future target positions are obtained by extrapolating the estimated trajectory. To improve localization accuracy, we analyze the relationship between the UAV path and the Cramér–Rao lower bound (CRLB) using the Fisher Information Matrix (FIM) and derive an observability-enhanced trajectory planning method. Real-flight experiments validate the framework, showing that accurate geolocation can be achieved in real time using only low-cost monocular bearing measurements. Full article

(This article belongs to the Special Issue Advancing UAV-Based Remote Sensing: Innovations, Techniques and Applications)

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36 pages, 64731 KB

Open AccessArticle

Automated Detection of Embankment Piping and Leakage Hazards Using UAV Visible Light Imagery: A Frequency-Enhanced Deep Learning Approach for Flood Risk Prevention

by Jian Liu, Zhonggen Wang, Renzhi Li, Ruxin Zhao and Qianlin Zhang

Remote Sens. 2025, 17(21), 3602; https://doi.org/10.3390/rs17213602 - 31 Oct 2025

Viewed by 792

Abstract

Embankment piping and leakage are primary causes of flood control infrastructure failure, accounting for more than 90% of embankment failures worldwide and posing significant threats to public safety and economic stability. Current manual inspection methods are labor-intensive, hazardous, and inadequate for emergency flood season monitoring, while existing automated approaches using thermal infrared imaging face limitations in cost, weather dependency, and deployment flexibility. This study addresses the critical scientific challenge of developing reliable, cost-effective automated detection systems for embankment safety monitoring using Unmanned Aerial Vehicle (UAV)-based visible light imagery. The fundamental problem lies in extracting subtle textural signatures of piping and leakage from complex embankment surface patterns under varying environmental conditions. To solve this challenge, we propose the Embankment-Frequency Network (EmbFreq-Net), a frequency-enhanced deep learning framework that leverages frequency-domain analysis to amplify hazard-related features while suppressing environmental noise. The architecture integrates dynamic frequency-domain feature extraction, multi-scale attention mechanisms, and lightweight design principles to achieve real-time detection capabilities suitable for emergency deployment and edge computing applications. This approach transforms traditional post-processing workflows into an efficient real-time edge computing solution, significantly improving computational efficiency and enabling immediate on-site hazard assessment. Comprehensive evaluations on a specialized embankment hazard dataset demonstrate that EmbFreq-Net achieves 77.68% mAP@0.5, representing a 4.19 percentage point improvement over state-of-the-art methods, while reducing computational requirements by 27.0% (4.6 vs. 6.3 Giga Floating-Point Operations (GFLOPs)) and model parameters by 21.7% (2.02M vs. 2.58M). These results demonstrate the method’s potential for transforming embankment safety monitoring from reactive manual inspection to proactive automated surveillance, thereby contributing to enhanced flood risk management and infrastructure resilience. Full article

(This article belongs to the Special Issue Advancing UAV-Based Remote Sensing: Innovations, Techniques and Applications)

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19 pages, 3766 KB

Open AccessArticle

Marginal Contribution Spectral Fusion Network for Remote Hyperspectral Soil Organic Matter Estimation

by Jiaze Tang, Dan Liu, Qisong Wang, Junbao Li, Jingxiao Liao and Jinwei Sun

Remote Sens. 2025, 17(16), 2806; https://doi.org/10.3390/rs17162806 - 13 Aug 2025

Viewed by 712

Abstract

Soil organic matter (SOM) is a fundamental indicator of soil health and a major component of the global carbon cycle; its accurate quantification is essential for sustainable agriculture. Conventional chemical assays yield only point-based soil measurements and miss the spatial distribution of soil elements; airborne hyperspectral remote sensing has emerged as a promising approach for the quantitative measurement and characterization of SOM. Inversion models translate hyperspectral data into quantitative SOM estimates. However, existing models rely solely on a single preprocessing pathway, limiting their ability to fully exploit available spectral information. We address these limitations by developing a marginal contribution-driven spectral fusion network (MC-SFNet) that conducts feature-level fusion of heterogeneous preprocessing outputs within a physics-guided deep architecture. Moreover, the combination of data-driven fusion and the Kubelka–Munk (KM) model yields more physically interpretable spectral features, advancing beyond prior purely data-driven methods. We validated MC-SFNet on a self-constructed remote sensing, high-throughput hyperspectral dataset comprising 200 black soil samples from Northeastern China (400–1000 nm, 256 bands). Experimental results show that our network reduces the RMSE by 10.7% relative to the prevailing generalized hyperspectral soil-inversion model. The proposed method provides a novel preprocessing pathway for forthcoming airborne high-throughput hyperspectral missions to extract soil-specific spectral information more effectively and further enhance large-scale SOM retrieval accuracy. Full article

(This article belongs to the Special Issue Advancing UAV-Based Remote Sensing: Innovations, Techniques and Applications)

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22 pages, 4189 KB

Open AccessArticle

A Hierarchical Path Planning Framework of Plant Protection UAV Based on the Improved D3QN Algorithm and Remote Sensing Image

by Haitao Fu, Zheng Li, Jian Lu, Weijian Zhang, Yuxuan Feng, Li Zhu, He Liu and Jian Li

Remote Sens. 2025, 17(15), 2704; https://doi.org/10.3390/rs17152704 - 4 Aug 2025

Cited by 1 | Viewed by 1089

Abstract

Traditional path planning algorithms often fail to simultaneously ensure operational efficiency, energy constraint compliance, and environmental adaptability in agricultural scenarios, thereby hindering the advancement of precision agriculture. To address these challenges, this study proposes a deep reinforcement learning algorithm, MoE-D3QN, which integrates a Mixture-of-Experts mechanism with a Bi-directional Long Short-Term Memory model. This design enhances the efficiency and robustness of UAV path planning in agricultural environments. Building upon this algorithm, a hierarchical coverage path planning framework is developed. Multi-level task maps are constructed using crop information extracted from Sentinel-2 remote sensing imagery. Additionally, a dynamic energy consumption model and a progressive composite reward function are incorporated to further optimize UAV path planning in complex farmland conditions. Simulation experiments reveal that in the two-level scenario, the MoE-D3QN algorithm achieves a coverage efficiency of 0.8378, representing an improvement of 37.84–63.38% over traditional algorithms and 19.19–63.38% over conventional reinforcement learning methods. The redundancy rate is reduced to 3.23%, which is 38.71–41.94% lower than traditional methods and 4.46–42.77% lower than reinforcement learning counterparts. In the three-level scenario, MoE-D3QN achieves a coverage efficiency of 0.8261, exceeding traditional algorithms by 52.13–71.45% and reinforcement learning approaches by 10.15–50.2%. The redundancy rate is further reduced to 5.26%, which is significantly lower than the 57.89–92.11% observed with traditional methods and the 15.57–18.98% reported for reinforcement learning algorithms. These findings demonstrate that the MoE-D3QN algorithm exhibits high-quality planning performance in complex farmland environments, indicating its strong potential for widespread application in precision agriculture. Full article

(This article belongs to the Special Issue Advancing UAV-Based Remote Sensing: Innovations, Techniques and Applications)

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19 pages, 55351 KB

Open AccessArticle

Improving UAV Remote Sensing Photogrammetry Accuracy Under Navigation Interference Using Anomaly Detection and Data Fusion

by Chen Meng, Haoyang Yang, Cuicui Jiang, Qinglei Hu and Dongyu Li

Remote Sens. 2025, 17(13), 2176; https://doi.org/10.3390/rs17132176 - 25 Jun 2025

Cited by 1 | Viewed by 1456

Abstract

Accurate and robust navigation is fundamental to Unmanned Aerial Vehicle (UAV) remote sensing operations. However, the susceptibility of UAV navigation sensors to diverse interference and malicious attacks can severely degrade positioning accuracy and compromise mission integrity. Addressing these vulnerabilities, this paper presents an integrated framework combining sensor anomaly detection with a Dynamic Adaptive Extended Kalman Filter (DAEKF) and federated filtering algorithms to bolster navigation resilience and accuracy for UAV remote sensing. Specifically, mathematical models for prevalent UAV sensor attacks were established. The proposed framework employs adaptive thresholding and residual consistency checks for the real-time identification and isolation of anomalous sensor measurements. Based on these detection outcomes, the DAEKF dynamically adjusts its sensor fusion strategies and noise covariance matrices. To further enhance the fault tolerance, a federated filtering architecture was implemented, utilizing adaptively weighted sub-filters based on assessed trustworthiness to effectively isolate faults. The efficacy of this framework was validated through rigorous experiments that involved real-world flight data and software-defined radio (SDR)-based Global Positioning System (GPS) spoofing, alongside simulated attacks. The results demonstrate exceptional performance, where the average anomaly detection accuracy exceeded 99% and the precision surpassed 98%. Notably, when benchmarked against traditional methods, the proposed system reduced navigation errors by a factor of approximately 2-3 under attack scenarios, which substantially enhanced the operational stability of the UAVs in challenging environments. Full article

(This article belongs to the Special Issue Advancing UAV-Based Remote Sensing: Innovations, Techniques and Applications)

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18 pages, 3938 KB

Open AccessArticle

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

Viewed by 1908

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. 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)

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21 pages, 14200 KB

Open AccessArticle

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

Cited by 1 | Viewed by 1418

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 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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31 pages, 99149 KB

Open AccessArticle

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

Cited by 5 | Viewed by 2204

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 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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