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Remote Sensing
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Advances and Innovative Applications of Unmanned Aerial Vehicles
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Advances and Innovative Applications of Unmanned Aerial Vehicles

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 26172

Special Issue Editor

Dr. Ming-Der Yang

E-Mail Website
Guest Editor
Distinguished Professor & Chair, Department of Civil Engineering, National Chung Hsing University, Taichung, Taiwan
Interests: image processing; AI; UAVs
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Theme and Scope

Recently, unmanned aerial vehicles (UAVs) are rapidly growing in remote sensing with their autonomy, flexibility, and broad range of application domains. UAVs offer possible civil and public domain applications in which single or multiple UAVs may be employed, including 3D scene reconstruction, imaging and mapping, utility inspection, anomaly detection and prevention, hazard monitoring, precision agriculture, etc. The prospective UAV applications are expected to be more broadly expanded in various fields, while deep learning technology is applied. Moreover, UAV applications will become more promising once 5G and AI technologies are applied in the near future. Aiming to promote an international exchange of scientific knowledge and experience in the field of UAV applications, we propose a Special Issue “Advances and Innovative Applications of Unmanned Aerial Vehicles”. This Special Issue will bring together academic and industrial papers in the field of UAVs to share the techniques and applications of consumer drones. We expect to publish 15+ valuable papers related to precious research outcomes. This Special Issue will provide a valuable opportunity to exchange ideas and expertise as well as network with research groups worldwide. Prospected authors are invited to submit their original contributions, surveys, and case studies that address novel research on and valuable applications of UAVs.

 The main topics include but are not limited to: 

  • 3D scene reconstruction of UAVs;
  • 5G era for UAV communications;
  • Ad-hoc networking and sensor networks for UAVs;
  • Autonomous UAVs;
  • Artificial intelligence in UAVs;
  • Big data and machine learning for UAVs;
  • Communication and control architectures for UAVs;
  • Cyber and physical security of UAVs.
  • Energy efficiency and optimization for UAV routing;
  • High-resolution UAV image processing and applications;
  • Optimal deployment for UAV swarm service;
  • Spectrum sensing techniques for UAV detection;
  • Simultaneous localization and mapping for UAVs;
  • Tracking, localization, navigation, and dynamic path planning of UAVs.

Dr. Ming-Der Yang
Guest Editor

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
  • image processing
  • AI
  • deep learning

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

Published Papers (5 papers)

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Research

20 pages, 6586 KiB  
Article
Large-Scale Particle Image Velocimetry to Measure Streamflow from Videos Recorded from Unmanned Aerial Vehicle and Fixed Imaging System
by Wen-Cheng Liu, Chien-Hsing Lu and Wei-Che Huang
Remote Sens. 2021, 13(14), 2661; https://doi.org/10.3390/rs13142661 - 6 Jul 2021
Cited by 20 | Viewed by 4016
Abstract
The accuracy of river velocity measurements plays an important role in the effective management of water resources. Various methods have been developed to measure river velocity. Currently, image-based techniques provide a promising approach to avoid physical contact with targeted water bodies by researchers. [...] Read more.
The accuracy of river velocity measurements plays an important role in the effective management of water resources. Various methods have been developed to measure river velocity. Currently, image-based techniques provide a promising approach to avoid physical contact with targeted water bodies by researchers. In this study, measured surface velocities collected under low flow and high flow conditions in the Houlong River, Taiwan, using large-scale particle image velocimetry (LSPIV) captured by an unmanned aerial vehicle (UAV) and a terrestrial fixed station were analyzed and compared. Under low flow conditions, the mean absolute errors of the measured surface velocities using LSPIV from a UAV with shooting heights of 9, 12, and 15 m fell within 0.055 ± 0.015 m/s, which was lower than that obtained using LSPIV on video recorded from a terrestrial fixed station (i.e., 0.34 m/s). The mean absolute errors obtained using LSPIV derived from UAV aerial photography at a flight height of 12 m without seeding particles and with different seeding particle densities were slightly different, and fell within the range of 0.095 ± 0.025 m/s. Under high flow conditions, the mean absolute errors associated with using LSPIV derived from terrestrial fixed photography and LSPIV derived from a UAV with flight heights of 32, 62, and 112 m were 0.46 m/s and 0.49 m/s, 0.27 m, and 0.97 m/s, respectively. A UAV flight height of 62 m yielded the best measured surface velocity result. Moreover, we also demonstrated that the optimal appropriate interrogation area and image acquisition time interval using LSPIV with a UAV were 16 × 16 pixels and 1/8 s, respectively. These two parameters should be carefully adopted to accurately measure the surface velocity of rivers. Full article
(This article belongs to the Special Issue Advances and Innovative Applications of Unmanned Aerial Vehicles)
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26 pages, 4511 KiB  
Article
Joint Task Offloading, Resource Allocation, and Security Assurance for Mobile Edge Computing-Enabled UAV-Assisted VANETs
by Yixin He, Daosen Zhai, Fanghui Huang, Dawei Wang, Xiao Tang and Ruonan Zhang
Remote Sens. 2021, 13(8), 1547; https://doi.org/10.3390/rs13081547 - 16 Apr 2021
Cited by 47 | Viewed by 4537
Abstract
In this paper, we propose a mobile edge computing (MEC)-enabled unmanned aerial vehicle (UAV)-assisted vehicular ad hoc network (VANET) architecture, based on which a number of vehicles are served by UAVs equipped with computation resource. Each vehicle has to offload its computing tasks [...] Read more.
In this paper, we propose a mobile edge computing (MEC)-enabled unmanned aerial vehicle (UAV)-assisted vehicular ad hoc network (VANET) architecture, based on which a number of vehicles are served by UAVs equipped with computation resource. Each vehicle has to offload its computing tasks to the proper MEC server on the UAV due to the limited computation ability. To counter the problems above, we first model and analyze the transmission model and the security assurance model from the vehicle to the MEC server on UAV, and the task computation model of the local vehicle and the edge UAV. Then, the vehicle offloading problem is formulated as a multi-objective optimization problem by jointly considering the task offloading, the resource allocation, and the security assurance. For tackling this hard problem, we decouple the multi-objective optimization problem as two subproblems and propose an efficient iterative algorithm to jointly make the MEC selection decision based on the criteria of load balancing and optimize the offloading ratio and the computation resource according to the Lagrangian dual decomposition. Finally, the simulation results demonstrate that our proposed scheme achieves significant performance superiority compared with other schemes in terms of the successful task processing ratio and the task processing delay. Full article
(This article belongs to the Special Issue Advances and Innovative Applications of Unmanned Aerial Vehicles)
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25 pages, 8440 KiB  
Article
A Novel Privacy Approach of Digital Aerial Images Based on Mersenne Twister Method with DNA Genetic Encoding and Chaos
by Fawad Masood, Wadii Boulila, Jawad Ahmad, Arshad, Syam Sankar, Saeed Rubaiee and William J. Buchanan
Remote Sens. 2020, 12(11), 1893; https://doi.org/10.3390/rs12111893 - 11 Jun 2020
Cited by 43 | Viewed by 4239
Abstract
Aerial photography involves capturing images from aircraft and other flying objects, including Unmanned Aerial Vehicles (UAV). Aerial images are used in many fields and can contain sensitive information that requires secure processing. We proposed an innovative new cryptosystem for the processing of aerial [...] Read more.
Aerial photography involves capturing images from aircraft and other flying objects, including Unmanned Aerial Vehicles (UAV). Aerial images are used in many fields and can contain sensitive information that requires secure processing. We proposed an innovative new cryptosystem for the processing of aerial images utilizing a chaos-based private key block cipher method so that the images are secure even on untrusted cloud servers. The proposed cryptosystem is based on a hybrid technique combining the Mersenne Twister (MT), Deoxyribonucleic Acid (DNA), and Chaotic Dynamical Rossler System (MT-DNA-Chaos) methods. The combination of MT with the four nucleotides and chaos sequencing creates an enhanced level of security for the proposed algorithm. The system is tested at three separate phases. The combined effects of the three levels improve the overall efficiency of the randomness of data. The proposed method is computationally agile, and offered more security than existing cryptosystems. To assess, this new system is examined against different statistical tests such as adjacent pixels correlation analysis, histogram consistency analyses and its variance, visual strength analysis, information randomness and uncertainty analysis, pixel inconsistency analysis, pixels similitude analyses, average difference, and maximum difference. These tests confirmed its validity for real-time communication purposes. Full article
(This article belongs to the Special Issue Advances and Innovative Applications of Unmanned Aerial Vehicles)
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40 pages, 30129 KiB  
Article
3D Registration and Integrated Segmentation Framework for Heterogeneous Unmanned Robotic Systems
by Haris Balta, Jasmin Velagic, Halil Beglerovic, Geert De Cubber and Bruno Siciliano
Remote Sens. 2020, 12(10), 1608; https://doi.org/10.3390/rs12101608 - 18 May 2020
Cited by 13 | Viewed by 4098
Abstract
The paper proposes a novel framework for registering and segmenting 3D point clouds of large-scale natural terrain and complex environments coming from a multisensor heterogeneous robotics system, consisting of unmanned aerial and ground vehicles. This framework involves data acquisition and pre-processing, 3D heterogeneous [...] Read more.
The paper proposes a novel framework for registering and segmenting 3D point clouds of large-scale natural terrain and complex environments coming from a multisensor heterogeneous robotics system, consisting of unmanned aerial and ground vehicles. This framework involves data acquisition and pre-processing, 3D heterogeneous registration and integrated multi-sensor based segmentation modules. The first module provides robust and accurate homogeneous registrations of 3D environmental models based on sensors’ measurements acquired from the ground (UGV) and aerial (UAV) robots. For 3D UGV registration, we proposed a novel local minima escape ICP (LME-ICP) method, which is based on the well known iterative closest point (ICP) algorithm extending it by the introduction of our local minima estimation and local minima escape mechanisms. It did not require any prior known pose estimation information acquired from sensing systems like odometry, global positioning system (GPS), or inertial measurement units (IMU). The 3D UAV registration has been performed using the Structure from Motion (SfM) approach. In order to improve and speed up the process of outliers removal for large-scale outdoor environments, we introduced the Fast Cluster Statistical Outlier Removal (FCSOR) method. This method was used to filter out the noise and to downsample the input data, which will spare computational and memory resources for further processing steps. Then, we co-registered a point cloud acquired from a laser ranger (UGV) and a point cloud generated from images (UAV) generated by the SfM method. The 3D heterogeneous module consists of a semi-automated 3D scan registration system, developed with the aim to overcome the shortcomings of the existing fully automated 3D registration approaches. This semi-automated registration system is based on the novel Scale Invariant Registration Method (SIRM). The SIRM provides the initial scaling between two heterogenous point clouds and provides an adaptive mechanism for tuning the mean scale, based on the difference between two consecutive estimated point clouds’ alignment error values. Once aligned, the resulting homogeneous ground-aerial point cloud is further processed by a segmentation module. For this purpose, we have proposed a system for integrated multi-sensor based segmentation of 3D point clouds. This system followed a two steps sequence: ground-object segmentation and color-based region-growing segmentation. The experimental validation of the proposed 3D heterogeneous registration and integrated segmentation framework was performed on large-scale datasets representing unstructured outdoor environments, demonstrating the potential and benefits of the proposed semi-automated 3D registration system in real-world environments. Full article
(This article belongs to the Special Issue Advances and Innovative Applications of Unmanned Aerial Vehicles)
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21 pages, 3953 KiB  
Article
Robust Motion Control for UAV in Dynamic Uncertain Environments Using Deep Reinforcement Learning
by Kaifang Wan, Xiaoguang Gao, Zijian Hu and Gaofeng Wu
Remote Sens. 2020, 12(4), 640; https://doi.org/10.3390/rs12040640 - 14 Feb 2020
Cited by 73 | Viewed by 7988
Abstract
In this paper, a novel deep reinforcement learning (DRL) method, and robust deep deterministic policy gradient (Robust-DDPG), is proposed for developing a controller that allows robust flying of an unmanned aerial vehicle (UAV) in dynamic uncertain environments. This technique is applicable in many [...] Read more.
In this paper, a novel deep reinforcement learning (DRL) method, and robust deep deterministic policy gradient (Robust-DDPG), is proposed for developing a controller that allows robust flying of an unmanned aerial vehicle (UAV) in dynamic uncertain environments. This technique is applicable in many fields, such as penetration and remote surveillance. The learning-based controller is constructed with an actor-critic framework, and can perform a dual-channel continuous control (roll and speed) of the UAV. To overcome the fragility and volatility of original DDPG, three critical learning tricks are introduced in Robust-DDPG: (1) Delayed-learning trick, providing stable learnings, while facing dynamic environments; (2) adversarial attack trick, improving policy’s adaptability to uncertain environments; (3) mixed exploration trick, enabling faster convergence of the model. The training experiments show great improvement in its convergence speed, convergence effect, and stability. The exploiting experiments demonstrate high efficiency in providing the UAV a shorter and smoother path. While, the generalization experiments verify its better adaptability to complicated, dynamic and uncertain environments, comparing to Deep Q Network (DQN) and DDPG algorithms. Full article
(This article belongs to the Special Issue Advances and Innovative Applications of Unmanned Aerial Vehicles)
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