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Multi-Sensor Data Fusion of Unmanned Aerial Vehicles (UAVs) Remote Sensing for Environmental Applications

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

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 13952

Special Issue Editors

Prof. Dr. Giuseppe Modica

E-Mail Website1 Website2
Guest Editor
Dipartimento di Scienze Veterinarie, Università degli Studi di Messina, Viale G. Palatucci s.n., 98168 Messina, Italy
Interests: land cover and land use change dynamics; satellite and UAV remote sensing; landscape analysis and interpretation; remote sensing of vegetation; geographic object-based image analysis; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Alireza Abbaspour

E-Mail Website
Guest Editor
Department of Automotive, Qualcomm Technologies Inc., San Diego, CA, USA
Interests: autonomous driving systems; safety, security, machine learning; anomaly detection; fault detection; intrusion detection system; materials; electric vehicles; unmanned aerial vehicles (UAV); faulty sensors; fault detection and isolation; abrupt fault; feedback linearization control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the use of multi-sensor imagery from unmanned aerial vehicles (UAVs) in environmental applications. Papers dealing with the issue of data fusion in the mapping and monitoring of vegetation are very welcome. The development of artificial intelligence (AI) and autonomous systems have increased the need for gathering accurate information about our surrounding environment in the recent decade. The application of UAV sensors and platforms for remote sensing and mapping the environment has received considerable attention among researchers due to the high spatial resolution, flexibility in the acquisition and sensor integration and cost-effectiveness of UAVs compared with manned aircraft. An UAV can be equipped with different sensors based on the desired task. A sensor fusion algorithm can be used to improve accuracy and thus retrieve a better image of the searched area. The use of UAVs offers new possibilities in vegetation classification and monitoring with very high levels of spatial and temporal detail. The increasingly widespread use of the geographic object-based image analysis (GEOBIA) approach in processing allows researchers to address the high spectral variability of the ultra-high-resolution imagery provided by UAVs. Machine learning algorithms and proper segmentation algorithms and software suites can significantly improve the speed and the quality of mapping and monitoring of vegetation.

The cooperation of a group of UAVs has also been considered as a solution for searching and mapping a large area that presents new challenges in the control of UAVs and for gathering and processing huge amounts of data collected from different sensors and UAVs. In addition, the detection and elimination of faulty information to improve the accuracy of remote sensing are new challenges that need to be addressed.

In this Special Issue, researchers are encouraged to submit valuable research findings that address the mentioned issues from a wide variety of perspectives. Welcomed topics include but are not limited to:

  • novel sensor fusion algorithms applied in UAVs for remote sensing;
  • fault detection and elimination from the gathered data in UAV’s sensors;
  • data processing/mining algorithms to label and interpret information received from sensors in UAV;
  • data fusion;
  • machine learning algorithms;
  • geographic object-based image analysis (GEOBIA) approach;
  • cooperative control of UAVs doing a remote sensing task;
  • cyberphysical threats and solutions for remote sensing using UAVs;
  • mapping and monitoring natural environments with UAV remote sensing;
  • co-registration of UAV imagery in monitoring tasks

Prof. Dr. Giuseppe Modica
Dr. Alireza Abbaspour
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
  • GEOBIA co-registration
  • segmentation algorithms
  • sensor fusion
  • sensor faults
  • cooperative monitoring
  • sensor data processing
  • sensor spoofing

Published Papers (4 papers)

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Research

14 pages, 788 KiB  
Article
Neural Network-Based Active Fault-Tolerant Control Design for Unmanned Helicopter with Additive Faults
by Sohrab Mokhtari, Alireza Abbaspour, Kang K. Yen and Arman Sargolzaei
Remote Sens. 2021, 13(12), 2396; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122396 - 19 Jun 2021
Cited by 16 | Viewed by 3145
Abstract
A novel adaptive neural network-based fault-tolerant control scheme is proposed for six degree-of-freedom nonlinear helicopter dynamic. The proposed approach can detect and mitigate actuators and sensors’ faults in real time. An adaptive observer-based on neural network (NN) and extended Kalman filter (EKF) is [...] Read more.
A novel adaptive neural network-based fault-tolerant control scheme is proposed for six degree-of-freedom nonlinear helicopter dynamic. The proposed approach can detect and mitigate actuators and sensors’ faults in real time. An adaptive observer-based on neural network (NN) and extended Kalman filter (EKF) is designed, which incorporates the helicopter’s dynamic model to detect faults in the actuators and navigation sensors. Based on the detected faults, an active fault-tolerant controller, including three loops of dynamic inversion, is designed to compensate for the occurred faults in real time. The simulation results showed that the proposed approach is able to detect and mitigate different types of faults on the helicopter actuators, and the helicopter tracks the desired trajectory without any interruption. Full article
(This article belongs to the Special Issue Multi-Sensor Data Fusion of Unmanned Aerial Vehicles (UAVs) Remote Sensing for Environmental Applications)
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16 pages, 1995 KiB  
Article
Autonomous Integrity Monitoring for Relative Navigation of Multiple Unmanned Aerial Vehicles
by Yuan Sun
Remote Sens. 2021, 13(8), 1483; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13081483 - 12 Apr 2021
Cited by 16 | Viewed by 2180
Abstract
Accurate and reliable relative navigation is the prerequisite to guarantee the effectiveness and safety of various multiple Unmanned Aerial Vehicles (UAVs) cooperation tasks, when absolute position information is unavailable or inaccurate. Among the UAV navigation techniques, Global Navigation Satellite System (GNSS) is widely [...] Read more.
Accurate and reliable relative navigation is the prerequisite to guarantee the effectiveness and safety of various multiple Unmanned Aerial Vehicles (UAVs) cooperation tasks, when absolute position information is unavailable or inaccurate. Among the UAV navigation techniques, Global Navigation Satellite System (GNSS) is widely used due to its worldwide coverage and simplicity in relative navigation. However, the observations of GNSS are vulnerable to different kinds of faults arising from transmission degradation, ionospheric scintillations, multipath, spoofing, and many other factors. In an effort to improve the reliability of multi-UAV relative navigation, an autonomous integrity monitoring method is proposed with a fusion of double differenced GNSS pseudoranges and Ultra Wide Band (UWB) ranging units. Specifically, the proposed method is designed to detect and exclude the fault observations effectively through a consistency check algorithm in the relative positioning system of the UAVs. Additionally, the protection level for multi-UAV relative navigation is estimated to evaluate whether the performance meets the formation flight and collision avoidance requirements. Simulated experiments derived from the real data are designed to verify the effectiveness of the proposed method in autonomous integrity monitoring for multi-UAV relative navigation. Full article
(This article belongs to the Special Issue Multi-Sensor Data Fusion of Unmanned Aerial Vehicles (UAVs) Remote Sensing for Environmental Applications)
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17 pages, 3960 KiB  
Article
Radiological Identification of Near-Surface Mineralogical Deposits Using Low-Altitude Unmanned Aerial Vehicle
by Peter G. Martin, Dean T. Connor, Natalia Estrada, Adel El-Turke, David Megson-Smith, Chris P. Jones, David K. Kreamer and Thomas B. Scott
Remote Sens. 2020, 12(21), 3562; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12213562 - 30 Oct 2020
Cited by 12 | Viewed by 2549
Abstract
An ever-increasing global population and unabating technological growth have resulted in a relentless appetite for mineral resources, namely rare earth elements, fuel minerals and those utilised in electronics applications, with the price of such species continuing to climb. In contrast to more established [...] Read more.
An ever-increasing global population and unabating technological growth have resulted in a relentless appetite for mineral resources, namely rare earth elements, fuel minerals and those utilised in electronics applications, with the price of such species continuing to climb. In contrast to more established large-scale and high-cost exploration methodologies, this work details the application of novel multi-rotor unmanned aerial vehicles equipped with miniaturised radiation detectors for the objective of undertaking resource exploration at lower costs, with greater autonomy and at considerably enhanced higher spatial resolutions; utilizing the ore material’s inherent low levels of characteristic radioactivity. As we demonstrate at the former Wooley Mine site in Arizona, USA, a legacy Cu/Fe prospect where the 600 by 275 m ore body (with a maximum deposit depth of 150 m), it is shown that such a fusion of commercially available low-altitude multi-rotor aerial technology combined with cutting-edge micro-electronics and detector materials is capable of accurately assessing the spatial distribution and associated radiogenic signatures of commercially valuable surface/near-surface ore bodies. This integrated system, deployed at an autonomously controlled consistent survey altitude and using constant grid transects/separations, is shown to be able to delineate the mineral-containing ore deposits on the site, the location(s) of former mine workings and other surface manifestations. Owing to its advantageous costs alongside its ease of operation and subsequent data-processing, through the adoption of this system, it is envisaged that less economically developed countries would now possess the means through which to evaluate and appropriately quantify their mineral wealth without the significant initial expenditure needed to equip themselves with otherwise prohibitively expensive technologies. Full article
(This article belongs to the Special Issue Multi-Sensor Data Fusion of Unmanned Aerial Vehicles (UAVs) Remote Sensing for Environmental Applications)
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23 pages, 47898 KiB  
Article
FFAU—Framework for Fully Autonomous UAVs
by Dário Pedro, João P. Matos-Carvalho, Fábio Azevedo, Ricardo Sacoto-Martins, Luís Bernardo, Luís Campos, José M. Fonseca and André Mora
Remote Sens. 2020, 12(21), 3533; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12213533 - 28 Oct 2020
Cited by 12 | Viewed by 3817
Abstract
Unmanned Aerial Vehicles (UAVs), although hardly a new technology, have recently gained a prominent role in many industries being widely used not only among enthusiastic consumers, but also in high demanding professional situations, and will have a massive societal impact over the coming [...] Read more.
Unmanned Aerial Vehicles (UAVs), although hardly a new technology, have recently gained a prominent role in many industries being widely used not only among enthusiastic consumers, but also in high demanding professional situations, and will have a massive societal impact over the coming years. However, the operation of UAVs is fraught with serious safety risks, such as collisions with dynamic obstacles (birds, other UAVs, or randomly thrown objects). These collision scenarios are complex to analyze in real-time, sometimes being computationally impossible to solve with existing State of the Art (SoA) algorithms, making the use of UAVs an operational hazard and therefore significantly reducing their commercial applicability in urban environments. In this work, a conceptual framework for both stand-alone and swarm (networked) UAVs is introduced, with a focus on the architectural requirements of the collision avoidance subsystem to achieve acceptable levels of safety and reliability. The SoA principles for collision avoidance against stationary objects are reviewed and a novel approach is described, using deep learning techniques to solve the computational intensive problem of real-time collision avoidance with dynamic objects. The proposed framework includes a web-interface allowing the full control of UAVs as remote clients with a supervisor cloud-based platform. The feasibility of the proposed approach was demonstrated through experimental tests using a UAV, developed from scratch using the proposed framework. Test flight results are presented for an autonomous UAV monitored from multiple countries across the world. Full article
(This article belongs to the Special Issue Multi-Sensor Data Fusion of Unmanned Aerial Vehicles (UAVs) Remote Sensing for Environmental Applications)
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