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Remote Sensing
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Near Surface Remote Sensing Using Unmanned Systems
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Near Surface Remote Sensing Using Unmanned Systems

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

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 11879

Special Issue Editor

Prof. Dr. Fridon Shubitidze

E-Mail Website
Guest Editor
Thayer School of Engineering, Dartmouth College, 14 Engineering Dr, Hanover, NH 03755, USA
Interests: remote sensing; magentic and electromagentic sensors; forward and inverse EM problems and methods; subsurface targets detection and classification; FPGA systems; nano-particle hyperthermia; numerical models; magnetic; electromagnetic; acoustic and optical sensors and unmanned systems for subsurface targets detection and classification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in the development of lightweight magnetic, electromagnetic, acoustic, and optical sensing technologies have created new applications for unmanned (ground robot and aerial) systems (USs). The sensing technologies mounted on USs can provide high-fidelity data to detect and identify hazardous subsurface targets safely and cost-effectively. This Special Issue is open to all contributors in the field of US remote sensing technologies (hardware and software) for mapping electromagnetic properties of near-surface soils and detecting and identifying targets of interest. We invite the submission of original research papers, case studies, and reviews to this Special Issue that extend and advance our scientific/technical understanding of the state-of-the-art in US remote sensing in areas that include, but are not limited to:

  • magnetic, electromagnetic, acoustic, seismic, and optical sensors and unmanned (ground robot and aerial) systems for subsurface target detection and identification;
  • combined US remote sensing technology and signal processing approaches for mapping electromagnetic properties of near-surface soils;
  • forward and inverse modeling for processing US remote sensing technologies datasets;
  • classification techniques, such as linear classifiers, support vector machines, quadratic classifiers, and neural networks, applied to data from US mounted sensors;
  • recent developments in and the integration of remote sensing technologies with US; and case studies during the mapping of electric and magnetic properties of soils for agriculture applications.

You may choose our Joint Special Issue in Drones.

Prof. Fridon Shubitidze
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

  • unmanned aerial systems
  • robotics
  • soil
  • remote sensing
  • UXO
  • magnetics
  • electromagnetic induction
  • acoustic
  • land mine
  • improvised explosive devices
  • classification
  • hazardous materials.

Published Papers (3 papers)

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Research

18 pages, 11538 KiB  
Article
Analysis of the Feasibility of UAS-Based EMI Sensing for Underground Utilities Detection and Mapping
by Caylin A. Hartshorn, Sven D. Isaacson, Benjamin E. Barrowes, Lee J. Perren, David Lozano and Fridon Shubitidze
Remote Sens. 2022, 14(16), 3973; https://doi.org/10.3390/rs14163973 - 16 Aug 2022
Cited by 3 | Viewed by 1466
Abstract
This paper investigates the feasibility of using a linear current sensing (LCS) technique integrated on an unmanned aerial system (UAS) for detecting and mapping underground infrastructure rapidly and cost-effectively. The LCS technique is based on data from a wide band of electromagnetic induction [...] Read more.
This paper investigates the feasibility of using a linear current sensing (LCS) technique integrated on an unmanned aerial system (UAS) for detecting and mapping underground infrastructure rapidly and cost-effectively. The LCS technique is based on data from a wide band of electromagnetic induction frequencies (50 kHz to 2 MHz) using a vector magnetic field gradiometer. This technique takes advantage of a slowly decaying secondary magnetic field in order to achieve greater standoff detection distance (1R2 vs. 1R6 for compact metallic targets during EMI sensing, where R is the distance from a target to the sensor). These secondary magnetic fields are produced by the excite current on long conductors, allowing detection at a distance of 10 meters or more. The system operates between tens of kHz to a few MHz and uses either an active EMI source or existing EM fields to excite this linear current on a long metallic subsurface target. Once excited, these linear currents produce a secondary magnetic field that is detected with an above ground triaxial magnetic field gradiometer. By moving and tracking its geolocation, the system outputs rich datasets sufficient to support high-fidelity forward and inverse EMI models for estimating the depth and orientation of deep underground long linear metallic infrastructure. The system’s hardware and its integration to a UAS system are outlined, along with the formulation of LCS theory, and numerical and experimental data are presented. The results illustrate that the LCS technique offers large standoff detection, is adaptable to UAS, and could be used effectively for detecting deep underground infrastructure such as wires and pipes. Full article
(This article belongs to the Special Issue Near Surface Remote Sensing Using Unmanned Systems)
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20 pages, 6718 KiB  
Article
The Joint UAV-Borne Magnetic Detection System and Cart-Mounted Time Domain Electromagnetic System for UXO Detection
by Yaxin Mu, Wupeng Xie and Xiaojuan Zhang
Remote Sens. 2021, 13(12), 2343; https://doi.org/10.3390/rs13122343 - 15 Jun 2021
Cited by 14 | Viewed by 2926
Abstract
For unexploded O=ordnance (UXO) detection, individual technology cannot achieve the best detection performance. The new detection mode of joint magnetic and electromagnetic method has attracted more and more attention. In this paper, a newly developed joint detection system is introduced, a multi-rotor UAV-based [...] Read more.
For unexploded O=ordnance (UXO) detection, individual technology cannot achieve the best detection performance. The new detection mode of joint magnetic and electromagnetic method has attracted more and more attention. In this paper, a newly developed joint detection system is introduced, a multi-rotor UAV-based magnetic system (UAVMAG) and a cart-based time-domain electromagnetic detection system (TDEM-Cart) are combined, and the cooperative processing of magnetic field and electromagnetic data is proposed. The result of the joint inversion fuses the feature vector retrieved from the magnetic field data and the feature vector inverted from the electromagnetic data, providing more accurate positioning results and richer information, which is favorable to locate and distinguish the UXO. Two field experiments are conducted, and the results show that when the joint system works in the full-coverage survey mode, both ferromagnetic and non-ferromagnetic metal targets can be detected, avoiding missed detections. In addition, when the joint system works in the cued survey mode, the detection efficiency is improved, the positioning accuracy of joint interpretation is less than 10 cm, and it shows satisfactory performance in the recognition of targets. Full article
(This article belongs to the Special Issue Near Surface Remote Sensing Using Unmanned Systems)
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21 pages, 32847 KiB  
Article
Real-Time Detection and Spatial Localization of Insulators for UAV Inspection Based on Binocular Stereo Vision
by Yunpeng Ma, Qingwu Li, Lulu Chu, Yaqin Zhou and Chang Xu
Remote Sens. 2021, 13(2), 230; https://doi.org/10.3390/rs13020230 - 11 Jan 2021
Cited by 87 | Viewed by 6562
Abstract
Unmanned aerial vehicles (UAVs) have become important tools for power transmission line inspection. Cameras installed on the platforms can efficiently obtain aerial images containing information about power equipment. However, most of the existing inspection systems cannot perform automatic real-time detection of transmission line [...] Read more.
Unmanned aerial vehicles (UAVs) have become important tools for power transmission line inspection. Cameras installed on the platforms can efficiently obtain aerial images containing information about power equipment. However, most of the existing inspection systems cannot perform automatic real-time detection of transmission line components. In this paper, an automatic transmission line inspection system incorporating UAV remote sensing with binocular visual perception technology is developed to accurately detect and locate power equipment in real time. The system consists of a UAV module, embedded industrial computer, binocular visual perception module, and control and observation module. Insulators, which are key components in power transmission lines as well as fault-prone components, are selected as the detection targets. Insulator detection and spatial localization in aerial images with cluttered backgrounds are interesting but challenging tasks for an automatic transmission line inspection system. A two-stage strategy is proposed to achieve precise identification of insulators. First, candidate insulator regions are obtained based on RGB-D saliency detection. Then, the skeleton structure of candidate insulator regions is extracted. We implement a structure search to realize the final accurate detection of insulators. On the basis of insulator detection results, we further propose a real-time object spatial localization method that combines binocular stereo vision and a global positioning system (GPS). The longitude, latitude, and height of insulators are obtained through coordinate conversion based on the UAV’s real-time flight data and equipment parameters. Experiment results in the actual inspection environment (220 kV power transmission line) show that the presented system meets the requirement of robustness and accuracy of insulator detection and spatial localization in practical engineering. Full article
(This article belongs to the Special Issue Near Surface Remote Sensing Using Unmanned Systems)
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