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Remote Sensing
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Rapid Processing and Analysis for Drone Applications
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Rapid Processing and Analysis for Drone Applications

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 16653

Special Issue Editors

Prof. Dr. Impyeong Lee

E-Mail Website
Guest Editor
Dept. of Geoinformatics, The University of Seoul, Seoulsiripdaero 163, Dongdaemnu-gu, Seoul 02504, Korea
Interests: drone; self-driving car; geospatial data science; mapping; localization; deep learning; real-time processing; sensor fusion; sensor calibration; object detection; semantic segmentation; change detection
Dr. Jinha Jung

E-Mail Website
Guest Editor
Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall Dr, West Lafayette, IN 47907, USA
Interests: UAV; geospatial data science; high performance computing; high throughput phenotyping; data fusion
Special Issues, Collections and Topics in MDPI journals
Dr. Kyoungah Choi

E-Mail Website
Guest Editor
Innovative Growth Department, Korea Agency for Infrastructure Technology Advancement, 286, Simin-daero, Dongan-gu, Anyang-si, Gyeonggi-do 14066, Korea
Interests: drone; self-driving car; smart construction; digital twin; real-time georeferencing; sensor fusion; machine learning; change detection; vision-based localization; indoor mapping; mobile mapping
Dr. Anjin Chang

E-Mail
Guest Editor
School of Engineering and Computing Sciences, Texas A&M University-Corpus Christi, 6300 Ocean Dr, Corpus Christi, TX 78414, USA
Interests: UAS/UAV; high-throughput phenotyping; precision agriculture; artificial intelligence; hyperspectral/LiDAR applications; disaster monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, drones have been widely adapted in various disciplines, such as photogrammetry/remote sensing, agriculture, forestry, disaster, and civil engineering, to name a few. Unlike conventional remote sensing data collection platforms such as satellites and manned aircrafts, drones make it easier to map areas of interest in a ultrafine spatial resolution with minimal crews and preparation time. Thanks to these advantages, drones are becoming a first-choice remote sensing platform when it requires urgent responses, for example, disaster response, monitoring of forest fires, search and rescue, facility inspection, surveillance of critical infrastructure, etc. As the use of drone technologies is gaining significant popularity in many disciplines, it is critically important that the entire process of acquiring, transmitting, processing, and analyzing drone sensory data is performed automatically almost in real time or quickly. Based on these motivations, this Special Issue is calling for original and innovative papers that address challenges in processing and analyzing drone sensory data quickly and automatically. This Special Issue is open to all researchers, and high-quality, unpublished submissions that address one or more of the following topics are solicited:

  • On-site calibration of a multisensor drone system;
  • Direct georeferencing of drone sensory data including images and lidar data;
  • Onboard processing and analysis for automatic drone operations;
  • Realtime object detection and mapping with drone sensory data;
  • On-site training for machine learning and deep learning with drone sensory data;
  • Fast machine learning and deep learning for drone applications;
  • Quick delivery and visualizing of drone sensory data and their processing results;
  • Fusion of drone sensory data with other remotely sensed or geospatial data;
  • Integrated systems for emergent drone applications such as disaster monitoring and damage assessment, facility investigation/inspection and maintenance, search and rescue, surveillance and reconnaissance, etc.

Prof. Dr. Impyeong Lee
Dr. Jinha Jung
Dr. Kyoungah Choi
Dr. Anjin Chang
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

  • Drone
  • Direct georeferencing
  • Sensor fusion
  • Realtime mapping
  • Deep learning
  • Fast processing
  • Emergency response
  • Disaster monitoring
  • Damage assessment
  • Search and rescue
  • Infrastructure monitoring

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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

15 pages, 6443 KiB  
Article
Evaluating the Correlation between Thermal Signatures of UAV Video Stream versus Photomosaic for Urban Rooftop Solar Panels
by Young-Seok Hwang, Stephan Schlüter, Jung-Joo Lee and Jung-Sup Um
Remote Sens. 2021, 13(23), 4770; https://doi.org/10.3390/rs13234770 - 25 Nov 2021
Cited by 3 | Viewed by 2796
Abstract
The unmanned aerial vehicle (UAV) autopilot flight to survey urban rooftop solar panels needs a certain flight altitude at a level that can avoid obstacles such as high-rise buildings, street trees, telegraph poles, etc. For this reason, the autopilot-based thermal imaging has severe [...] Read more.
The unmanned aerial vehicle (UAV) autopilot flight to survey urban rooftop solar panels needs a certain flight altitude at a level that can avoid obstacles such as high-rise buildings, street trees, telegraph poles, etc. For this reason, the autopilot-based thermal imaging has severe data redundancy—namely, that non-solar panel area occupies more than 99% of ground target, causing a serious lack of the thermal markers on solar panels. This study aims to explore the correlations between the thermal signatures of urban rooftop solar panels obtained from a UAV video stream and autopilot-based photomosaic. The thermal signatures of video imaging are strongly correlated (0.89–0.99) to those of autopilot-based photomosaics. Furthermore, the differences in the thermal signatures of solar panels between the video and photomosaic are aligned in the range of noise equivalent differential temperature with a 95% confidence level. The results of this study could serve as a valuable reference for employing video stream-based thermal imaging to urban rooftop solar panels. Full article
(This article belongs to the Special Issue Rapid Processing and Analysis for Drone Applications)
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30 pages, 13181 KiB  
Article
Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty
by Juan Sandino, Frederic Maire, Peter Caccetta, Conrad Sanderson and Felipe Gonzalez
Remote Sens. 2021, 13(21), 4481; https://doi.org/10.3390/rs13214481 - 8 Nov 2021
Cited by 28 | Viewed by 8885
Abstract
Recent advances in autonomy of unmanned aerial vehicles (UAVs) have increased their use in remote sensing applications, such as precision agriculture, biosecurity, disaster monitoring, and surveillance. However, onboard UAV cognition capabilities for understanding and interacting in environments with imprecise or partial observations, for [...] Read more.
Recent advances in autonomy of unmanned aerial vehicles (UAVs) have increased their use in remote sensing applications, such as precision agriculture, biosecurity, disaster monitoring, and surveillance. However, onboard UAV cognition capabilities for understanding and interacting in environments with imprecise or partial observations, for objects of interest within complex scenes, are limited, and have not yet been fully investigated. This limitation of onboard decision-making under uncertainty has delegated the motion planning strategy in complex environments to human pilots, which rely on communication subsystems and real-time telemetry from ground control stations. This paper presents a UAV-based autonomous motion planning and object finding system under uncertainty and partial observability in outdoor environments. The proposed system architecture follows a modular design, which allocates most of the computationally intensive tasks to a companion computer onboard the UAV to achieve high-fidelity results in simulated environments. We demonstrate the system with a search and rescue (SAR) case study, where a lost person (victim) in bushland needs to be found using a sub-2 kg quadrotor UAV. The navigation problem is mathematically formulated as a partially observable Markov decision process (POMDP). A motion strategy (or policy) is obtained once a POMDP is solved mid-flight and in real time using augmented belief trees (ABT) and the TAPIR toolkit. The system’s performance was assessed using three flight modes: (1) mission mode, which follows a survey plan and used here as the baseline motion planner; (2) offboard mode, which runs the POMDP-based planner across the flying area; and (3) hybrid mode, which combines mission and offboard modes for improved coverage in outdoor scenarios. Results suggest the increased cognitive power added by the proposed motion planner and flight modes allow UAVs to collect more accurate victim coordinates compared to the baseline planner. Adding the proposed system to UAVs results in improved robustness against potential false positive readings of detected objects caused by data noise, inaccurate detections, and elevated complexity to navigate in time-critical applications, such as SAR. Full article
(This article belongs to the Special Issue Rapid Processing and Analysis for Drone Applications)
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18 pages, 5625 KiB  
Article
A Novel Method for Fast Positioning of Non-Standardized Ground Control Points in Drone Images
by Zheng Zhu, Tengfei Bao, Yuhan Hu and Jian Gong
Remote Sens. 2021, 13(15), 2849; https://doi.org/10.3390/rs13152849 - 21 Jul 2021
Cited by 7 | Viewed by 3450
Abstract
Positioning the pixels of ground control points (GCPs) in drone images is an issue of great concern in the field of drone photogrammetry. The current mainstream automatic approaches are based on standardized markers, such as circular coded targets and point coded targets. There [...] Read more.
Positioning the pixels of ground control points (GCPs) in drone images is an issue of great concern in the field of drone photogrammetry. The current mainstream automatic approaches are based on standardized markers, such as circular coded targets and point coded targets. There is no denying that introducing standardized markers improves the efficiency of positioning GCP pixels. However, the low flexibility leads to some drawbacks, such as the heavy logistical input in placing and maintaining GCP markers. Especially as drone photogrammetry steps into the era of large scenes, the logistical input in maintaining GCP markers becomes much more costly. This paper proposes a novel positioning method applicable for non-standardized GCPs. Firstly, regions of interest (ROIs) are extracted from drone images with stereovision technologies. Secondly, the quality of ROIs is evaluated using image entropy, and then the outliers are filtered by an adjusted boxplot. Thirdly, pixels of interest are searched with a corner detector, and the precise imagery coordinates are obtained by subpixel optimization. Finally, the verification was carried out in an urban scene, and the results show that this method has good applicability to the GCPs on road traffic signs, and the accuracy rate is over 95%. Full article
(This article belongs to the Special Issue Rapid Processing and Analysis for Drone Applications)
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