Special Issue "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: 31 October 2021.

Special Issue Editor

Prof. Fridon Shubitidze
E-Mail Website
Guest Editor
Dartmouth College, Thayer School of Engineering, 14 Engineering Dr. Hanover, NH 03755, USA
Interests: electromagnetic sensing technologies; detection and discrimination of sub-surface objects; linear and non-linear inverse-scattering; induced geo-electromagnetic fields; magnetic nanoparticle hyperthermia for cancer treatment and imaging; remote sensing; magnetic, electromagnetic, acoustic, and optical sensors and unmanned systems for subsurface target detection and classification
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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.

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 papers will be 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 2400 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 (1 paper)

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Research

Open AccessArticle
Real-Time Detection and Spatial Localization of Insulators for UAV Inspection Based on Binocular Stereo Vision
Remote Sens. 2021, 13(2), 230; https://doi.org/10.3390/rs13020230 - 11 Jan 2021
Cited by 1 | Viewed by 615
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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