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UAVs and Remote Sensing for Infrastructure, Environment, Defense, and Security Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (30 October 2019) | Viewed by 66433

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Dr. Paul-François Paradis

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Guest Editor

National Optics Institute, Remote Sensing Group, Quebec, Canada
Interests: lidars; hyperspectral imaging; UAVs; non-contact characterization; materials processing

Special Issue Information

Dear Colleagues,

The last few years have seen an outstanding development of UAVs of a myriad of sizes, geometries, and configurations, with an exponential improvement in endurance and autonomy. Their performance and capabilities have allowed applications ranging from military operations (ISR, communications, attack, etc.) to parcel delivery, and included rescue assistance during volcano eruptions, cinematography, archeology, real estate valuation, and many more. Although the use of UAVs in remote sensing has already displayed spectacular achievements in various areas, notably in agriculture and forestry, the increasing availability of both sensors and UAVs combined to lower costs and ease of use opened the way to ubiquitous and virtually unlimited applications.

This Special Issue calls for papers reporting on disruptive, innovative, and original contributions to R&D related to UAVs and remote sensing in particular for infrastructure, environment, manufacturing, defense, and security applications.

Possible topics include, but are not limited to:

Sensor development for UAVs for tailored remote sensing applications
Payload integration
Implementation of remote sensing schemes with UAVs
Tailored UAV design or architectures for remote sensing
Human–UAV collaboration
Swarms of UAVs

Dr. Paul-François Paradis
Guest Editor

Manuscript Submission Information

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Keywords

Payloads
lidars
active/passive sensors
multispectral/hyperspectral imaging
UAVs
remote sensing

Published Papers (12 papers)

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Research

20 pages, 4832 KiB

Open AccessArticle

Traffic Patrolling Routing Problem with Drones in an Urban Road System

by He Luo, Peng Zhang, Jiajie Wang, Guoqiang Wang and Fanhe Meng

Sensors 2019, 19(23), 5164; https://doi.org/10.3390/s19235164 - 25 Nov 2019

Cited by 22 | Viewed by 4078

Abstract

The remarkable development of various sensor equipment and communication technologies has stimulated many application platforms of automation. A drone is a sensing platform with strong environmental adaptability and expandability, which is widely used in aerial photography, transmission line inspection, remote sensing mapping, auxiliary communication, traffic patrolling, and other fields. A drone is an effective supplement to the current patrolling business in road traffic patrolling with complex urban buildings and road conditions and a limited ground perspective. However, the limited endurance of patrol drones can be directly solved by vehicles that cooperate with drones on patrolling missions. In this paper, we first proposed and studied the traffic patrolling routing problem with drones (TPRP-D) in an urban road system. Considering road network equations and the heterogeneity of patrolling tasks in the actual patrolling process, we modeled the problem as a double-layer arc routing problem (DL-ARP). Based on graph theory and related research work, we present the mixed integer linear programming formulations and two-stage heuristic solution approaches to solve practical-sized problems. Through analysis of numerical experiments, the solution method proposed in this paper can quickly provide an optimal path planning scheme for different test sets, which can save 9%–16% of time compared with traditional vehicle patrol. At the same time, we analyze several relevant parameters of the patrol process to determine the effect of coordinated traffic patrol. Finally, a case study was completed to verify the practicability of the algorithm. Full article

(This article belongs to the Special Issue UAVs and Remote Sensing for Infrastructure, Environment, Defense, and Security Applications)

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16 pages, 3963 KiB

Open AccessArticle

Suitability of a Non-Dispersive Infrared Methane Sensor Package for Flux Quantification Using an Unmanned Aerial Vehicle

by Adil Shah, Joseph Pitt, Khristopher Kabbabe and Grant Allen

Sensors 2019, 19(21), 4705; https://doi.org/10.3390/s19214705 - 29 Oct 2019

Cited by 11 | Viewed by 3792

Abstract

Point-source methane emission flux quantification is required to help constrain the global methane budget. Facility-scale fluxes can be derived using in situ methane mole fraction sampling, near-to-source, which may be acquired from an unmanned aerial vehicle (UAV) platform. We test a new non-dispersive infrared methane sensor by mounting it onto a small UAV, which flew downwind of a controlled methane release. Nine UAV flight surveys were conducted on a downwind vertical sampling plane, perpendicular to mean wind direction. The sensor was first packaged in an enclosure prior to sampling which contained a pump and a recording computer, with a total mass of 1.0 kg. The packaged sensor was then characterised to derive a gain factor of 0.92 ± 0.07, independent of water mole fraction, and an Allan deviation precision (at 1 Hz) of ±1.16 ppm. This poor instrumental precision and possible short-term drifts made it non-trivial to define a background mole fraction during UAV surveys, which may be important where any measured signal is small compared to sources of instrumental uncertainty and drift. This rendered the sensor incapable of deriving a meaningful flux from UAV sampling for emissions of the order of 1 g s⁻¹. Nevertheless, the sensor may indeed be useful when sampling mole fraction enhancements of the order of at least 10 ppm (an order of magnitude above the 1 Hz Allan deviation), either from stationary ground-based sampling (in baseline studies) or from mobile sampling downwind of sources with greater source flux than those observed in this study. While many methods utilising low-cost sensors to determine methane flux are being developed, this study highlights the importance of adequately characterising and testing all new sensors before they are used in scientific research. Full article

(This article belongs to the Special Issue UAVs and Remote Sensing for Infrastructure, Environment, Defense, and Security Applications)

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18 pages, 7445 KiB

Open AccessArticle

Dual-Probe Near-Field Phaseless Antenna Measurement System on Board a UAV

by María García Fernández, Yuri Álvarez López and Fernando Las-Heras

Sensors 2019, 19(21), 4663; https://doi.org/10.3390/s19214663 - 27 Oct 2019

Cited by 11 | Viewed by 4388

Abstract

On-site antenna measurement has been recently attracting an increasing interest in order to assess the antenna performance in real operational environments. The complexity and cost of these kind of measurements have been significantly cut down due to recent developments in unmanned aerial vehicles’ (UAVs) hardware and antenna measurement post-processing techniques. In particular, the introduction of positioning and geo-referring subsystems capable of providing centimeter-level accuracy together with the use of phase retrieval techniques and near-field to far-field transformation algorithms, have enabled near field measurements using UAVs. This contribution presents an improved UAV-based on-site antenna measurement system. On the one hand, the simultaneous acquisition on two measurement surfaces has been introduced and calibrated properly, thus reducing geo-referring uncertainties and flight time. On the other hand, the positioning and geo-referring subsystem has been enhanced by means of a dual-band real time kinematics (RTK) unit. The system capabilities were validated by measuring an offset reflector antenna, and the results were compared with the measurements at the spherical range in the anechoic chamber and with the measurements collected with a previous version of the implemented system. Full article

(This article belongs to the Special Issue UAVs and Remote Sensing for Infrastructure, Environment, Defense, and Security Applications)

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19 pages, 12562 KiB

Open AccessArticle

Road Extraction from Unmanned Aerial Vehicle Remote Sensing Images Based on Improved Neural Networks

by Yuxia Li, Bo Peng, Lei He, Kunlong Fan, Zhenxu Li and Ling Tong

Sensors 2019, 19(19), 4115; https://doi.org/10.3390/s19194115 - 23 Sep 2019

Cited by 22 | Viewed by 2980

Abstract

Roads are vital components of infrastructure, the extraction of which has become a topic of significant interest in the field of remote sensing. Because deep learning has been a popular method in image processing and information extraction, researchers have paid more attention to extracting road using neural networks. This article proposes the improvement of neural networks to extract roads from Unmanned Aerial Vehicle (UAV) remote sensing images. D-Linknet was first considered for its high performance; however, the huge scale of the net reduced computational efficiency. With a focus on the low computational efficiency problem of the popular D-LinkNet, this article made some improvements: (1) Replace the initial block with a stem block. (2) Rebuild the entire network based on ResNet units with a new structure, allowing for the construction of an improved neural network D-Linknetplus. (3) Add a 1 × 1 convolution layer before DBlock to reduce the input feature maps, reducing parameters and improving computational efficiency. Add another 1 × 1 convolution layer after DBlock to recover the required number of output channels. Accordingly, another improved neural network B-D-LinknetPlus was built. Comparisons were performed between the neural nets, and the verification were made with the Massachusetts Roads Dataset. The results show improved neural networks are helpful in reducing the network size and developing the precision needed for road extraction. Full article

(This article belongs to the Special Issue UAVs and Remote Sensing for Infrastructure, Environment, Defense, and Security Applications)

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11 pages, 17443 KiB

Open AccessArticle

Assessment of CNN-Based Methods for Individual Tree Detection on Images Captured by RGB Cameras Attached to UAVs

by Anderson Aparecido dos Santos, José Marcato Junior, Márcio Santos Araújo, David Robledo Di Martini, Everton Castelão Tetila, Henrique Lopes Siqueira, Camila Aoki, Anette Eltner, Edson Takashi Matsubara, Hemerson Pistori, Raul Queiroz Feitosa, Veraldo Liesenberg and Wesley Nunes Gonçalves

Sensors 2019, 19(16), 3595; https://doi.org/10.3390/s19163595 - 18 Aug 2019

Cited by 120 | Viewed by 9917

Abstract

Detection and classification of tree species from remote sensing data were performed using mainly multispectral and hyperspectral images and Light Detection And Ranging (LiDAR) data. Despite the comparatively lower cost and higher spatial resolution, few studies focused on images captured by Red-Green-Blue (RGB) sensors. Besides, the recent years have witnessed an impressive progress of deep learning methods for object detection. Motivated by this scenario, we proposed and evaluated the usage of Convolutional Neural Network (CNN)-based methods combined with Unmanned Aerial Vehicle (UAV) high spatial resolution RGB imagery for the detection of law protected tree species. Three state-of-the-art object detection methods were evaluated: Faster Region-based Convolutional Neural Network (Faster R-CNN), YOLOv3 and RetinaNet. A dataset was built to assess the selected methods, comprising 392 RBG images captured from August 2018 to February 2019, over a forested urban area in midwest Brazil. The target object is an important tree species threatened by extinction known as Dipteryx alata Vogel (Fabaceae). The experimental analysis delivered average precision around 92% with an associated processing times below 30 miliseconds. Full article

(This article belongs to the Special Issue UAVs and Remote Sensing for Infrastructure, Environment, Defense, and Security Applications)

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21 pages, 8603 KiB

Open AccessArticle

Ground Control Point-Free Unmanned Aerial Vehicle-Based Photogrammetry for Volume Estimation of Stockpiles Carried on Barges

by Haiqing He, Ting Chen, Huaien Zeng and Shengxiang Huang

Sensors 2019, 19(16), 3534; https://doi.org/10.3390/s19163534 - 13 Aug 2019

Cited by 24 | Viewed by 4956

Abstract

In this study, an approach using ground control point-free unmanned aerial vehicle (UAV)-based photogrammetry is proposed to estimate the volume of stockpiles carried on barges in a dynamic environment. Compared with similar studies regarding UAVs, an indirect absolute orientation based on the geometry of the vessel is used to establish a custom-built framework that can provide a unified reference instead of prerequisite ground control points (GCPs). To ensure sufficient overlap and reduce manual intervention, the stereo images are extracted from a UAV video for aerial triangulation. The region of interest is defined to exclude the area of water in all UAV images using a simple linear iterative clustering algorithm, which segments the UAV images into superpixels and helps to improve the accuracy of image matching. Structure-from-motion is used to recover three-dimensional geometry from the overlapping images without assistance of exterior parameters obtained from the airborne global positioning system and inertial measurement unit. Then, the semi-global matching algorithm is used to generate stockpile-covered and stockpile-free surface models. These models are oriented into a custom-built framework established by the known distance, such as the length and width of the vessel, and they do not require GCPs for coordinate transformation. Lastly, the volume of a stockpile is estimated by multiplying the height difference between the stockpile-covered and stockpile-free surface models by the size of the grid that is defined using the resolution of these models. Results show that a relatively small deviation of approximately ±2% between the volume estimated by UAV photogrammetry and the volume calculated by traditional manual measurement was obtained. Therefore, the proposed approach can be considered the better solution for the volume measurement of stockpiles carried on barges in a dynamic environment because UAV-based photogrammetry not only attains superior density and spatial object accuracy but also remarkably reduces data collection time. Full article

(This article belongs to the Special Issue UAVs and Remote Sensing for Infrastructure, Environment, Defense, and Security Applications)

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15 pages, 8304 KiB

Open AccessArticle

Fault Detection in Power Equipment via an Unmanned Aerial System Using Multi Modal Data

by Bushra Jalil, Giuseppe Riccardo Leone, Massimo Martinelli, Davide Moroni, Maria Antonietta Pascali and Andrea Berton

Sensors 2019, 19(13), 3014; https://doi.org/10.3390/s19133014 - 09 Jul 2019

Cited by 73 | Viewed by 10229

Abstract

The power transmission lines are the link between power plants and the points of consumption, through substations. Most importantly, the assessment of damaged aerial power lines and rusted conductors is of extreme importance for public safety; hence, power lines and associated components must be periodically inspected to ensure a continuous supply and to identify any fault and defect. To achieve these objectives, recently, Unmanned Aerial Vehicles (UAVs) have been widely used; in fact, they provide a safe way to bring sensors close to the power transmission lines and their associated components without halting the equipment during the inspection, and reducing operational cost and risk. In this work, a drone, equipped with multi-modal sensors, captures images in the visible and infrared domain and transmits them to the ground station. We used state-of-the-art computer vision methods to highlight expected faults (i.e., hot spots) or damaged components of the electrical infrastructure (i.e., damaged insulators). Infrared imaging, which is invariant to large scale and illumination changes in the real operating environment, supported the identification of faults in power transmission lines; while a neural network is adapted and trained to detect and classify insulators from an optical video stream. We demonstrate our approach on data captured by a drone in Parma, Italy. Full article

(This article belongs to the Special Issue UAVs and Remote Sensing for Infrastructure, Environment, Defense, and Security Applications)

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26 pages, 6516 KiB

Open AccessArticle

UAV Positioning for Throughput Maximization Using Deep Learning Approaches

by Yirga Yayeh Munaye, Hsin-Piao Lin, Abebe Belay Adege and Getaneh Berie Tarekegn

Sensors 2019, 19(12), 2775; https://doi.org/10.3390/s19122775 - 20 Jun 2019

Cited by 35 | Viewed by 5750

Abstract

The use of unmanned aerial vehicles (UAVs) as a communication platform has great practical importance for future wireless networks, especially for on-demand deployment for temporary and emergency conditions. The user throughput estimation in a wireless system depends on the data traffic load and the available capacity to support that load. In UAV-assisted communication, the position of the UAV is one major factor that affects the capacity available to the data flows being served. This study applies multi-layer perceptron (MLP) and long short term memory (LSTM) approaches to determine the position of a UAV that maximizes the overall system performance and user throughput. To analyze and evaluate the system performance, we apply the hybrid of MLP-LSTM for classification regression tasks and K-means algorithms for automatic clustering of classes. The implementation of our work is done through TensorFlow packages. The performance of our proposed system is compared with other approaches to give accurate and novel results for both classification and regression tasks of the user throughput maximization and UAV positioning. According to the results, 98% of the user throughput maximization accuracy is correctly classified. Moreover, the UAV positioning provides accuracy levels of 94.73%, 98.33%, and 99.53% for original datasets (scenario 1), reduced features on the estimated values of user throughput at each grid point (scenario 2), and reduced feature datasets collected on different days and grid points achieved maximum throughput (scenario 3), respectively. Full article

(This article belongs to the Special Issue UAVs and Remote Sensing for Infrastructure, Environment, Defense, and Security Applications)

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21 pages, 3925 KiB

Open AccessArticle

Comparison of Leaf Area Index, Surface Temperature, and Actual Evapotranspiration Estimated Using the METRIC Model and In Situ Measurements

by Arturo Reyes-González, Jeppe Kjaersgaard, Todd Trooien, David G. Reta-Sánchez, Juan I. Sánchez-Duarte, Pablo Preciado-Rangel and Manuel Fortis-Hernández

Sensors 2019, 19(8), 1857; https://doi.org/10.3390/s19081857 - 18 Apr 2019

Cited by 15 | Viewed by 3737

Abstract

The verification of remotely sensed estimates of surface variables is essential for any remote sensing study. The objective of this study was to compare leaf area index (LAI), surface temperature (Ts), and actual evapotranspiration (ETa), estimated using the remote sensing-based METRIC model and in situ measurements collected at the satellite overpass time. The study was carried out at a commercial corn field in eastern South Dakota. Six clear-sky images from Landsat 7 and Landsat 8 (Path 29, Row 29) were processed and used for the assessment. LAI and Ts were measured in situ, and ETa was estimated using an atmometer and independent crop coefficients. The results revealed good agreement between the variables measured in situ and estimated by the METRIC model. LAI showed r² = 0.76, and RMSE = 0.59 m² m⁻², the Ts comparison had an agreement of r² = 0.87 and RMSE 1.24 °C, and ETa presented r² = 0.89 and RMSE = 0.71 mm day⁻¹. Full article

(This article belongs to the Special Issue UAVs and Remote Sensing for Infrastructure, Environment, Defense, and Security Applications)

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22 pages, 5012 KiB

Open AccessArticle

Development of Radio-Frequency Sensor Wake-Up with Unmanned Aerial Vehicles as an Aerial Gateway

by Jianfei Chen, Zhaohua Dai and ZhiQiang Chen

Sensors 2019, 19(5), 1047; https://doi.org/10.3390/s19051047 - 01 Mar 2019

Cited by 15 | Viewed by 6504

Abstract

The advent of autonomous navigation, positioning, and general robotics technologies has enabled the improvement of small to miniature-sized unmanned aerial vehicles (UAVs, or ‘drones’) and their wide uses in engineering practice. Recent research endeavors further envision a systematic integration of aerial drones and traditional contact-based or ground-based sensors, leading to an aerial–ground wireless sensor network (AG-WSN), in which the UAV serves as both a gateway besides and a remote sensing platform. This paper serves two goals. First, we will review the recent development in architecture, design, and algorithms related to UAVs as a gateway and particularly illustrate its nature in realizing an opportunistic sensing network. Second, recognizing the opportunistic sensing need, we further aim to focus on achieving energy efficiency through developing an active radio frequency (RF)-based wake-up mechanism for aerial–ground data transmission. To prove the effectiveness of energy efficiency, several sensor wake-up solutions are physically implemented and evaluated. The results show that the RF-based wake-up mechanism can potentially save more than 98.4% of the energy that the traditional duty-cycle method would otherwise consume, and 96.8% if an infrared-receiver method is used. Full article

(This article belongs to the Special Issue UAVs and Remote Sensing for Infrastructure, Environment, Defense, and Security Applications)

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27 pages, 7104 KiB

Open AccessArticle

Preflight Contingency Planning Approach for Fixed Wing UAVs with Engine Failure in the Presence of Winds

by Bulent Ayhan, Chiman Kwan, Bence Budavari, Jude Larkin and David Gribben

Sensors 2019, 19(2), 227; https://doi.org/10.3390/s19020227 - 09 Jan 2019

Cited by 10 | Viewed by 4336

Abstract

Preflight contingency planning that utilizes wind forecast in path planning can be highly beneficial to unmanned aerial vehicles (UAV) operators in preventing a possible mishap of the UAV. This especially becomes more important if the UAV has an engine failure resulting in the loss of all its thrust. Wind becomes a significant factor in determining reachability of the emergency landing site in a contingency like this. The preflight contingency plans can guide the UAV operators about how to glide the aircraft to the designated emergency landing site to make a safe landing. The need for a preflight or in-flight contingency plan is even more obvious in the case of a communication loss between the UAV operator and UAV since the UAV will then need to make the forced landing autonomously without the operator. In this paper, we introduce a preflight contingency planning approach that automates the forced landing path generation process for UAVs with engine failure. The contingency path generation aims true reachability to the emergency landing site by including the final approach part of the path in forecast wind conditions. In the contingency path generation, no-fly zones that could be in the area are accounted for and the contingency flight paths do not pass through them. If no plans can be found that fulfill reachability in the presence of no-fly zones, only then, as a last resort, the no-fly zone avoidance rule is relaxed. The contingency path generation utilizes hourly forecast wind data from National Oceanic and Atmospheric Administration for the geographical area of interest and time of the flight. Different from past works, we use trochoidal paths instead of Dubins curves and incorporate wind as a parameter in the contingency path design. Full article

(This article belongs to the Special Issue UAVs and Remote Sensing for Infrastructure, Environment, Defense, and Security Applications)

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15 pages, 3113 KiB

Open AccessArticle

Non-Destructive Trace Detection of Explosives Using Pushbroom Scanning Hyperspectral Imaging System

by Siddharth Chaudhary, Sarawut Ninsawat and Tai Nakamura

Sensors 2019, 19(1), 97; https://doi.org/10.3390/s19010097 - 28 Dec 2018

Cited by 16 | Viewed by 4998

Abstract

The aim of this study was to investigate the potential of the non-destructive hyperspectral imaging system (HSI) and accuracy of the model developed using Support Vector Machine (SVM) for determining trace detection of explosives. Raman spectroscopy has been used in similar studies, but no study has been published which is based on measurement of reflectance from hyperspectral sensor for trace detection of explosives. HSI used in this study has an advantage over existing techniques due to its combination of imaging system and spectroscopy, along with being contactless and non-destructive in nature. Hyperspectral images of the chemical were collected using the BaySpec hyperspectral sensor which operated in the spectral range of 400–1000 nm (144 bands). Image processing was applied on the acquired hyperspectral image to select the region of interest (ROI) and to extract the spectral reflectance of the chemicals which were stored as spectral library. Principal Component Analysis (PCA) and first derivative was applied to reduce the high dimensionality of the image and to determine the optimal wavelengths between 400 and 1000 nm. In total, 22 out of 144 wavelengths were selected by analysing the loadings of principal components (PC). SVM was used to develop the classification model. SVM model established on the whole spectrum from 400 to 1000 nm achieved an accuracy of 81.11%, whereas an accuracy of 77.17% with less computational load was achieved when SVM model was established on the optimal wavelengths selected. The results of the study demonstrate that the hyperspectral imaging system along with SVM is a promising tool for trace detection of explosives. Full article

(This article belongs to the Special Issue UAVs and Remote Sensing for Infrastructure, Environment, Defense, and Security Applications)

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