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Near Surface Remote Sensing Using Unmanned Systems

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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 15690

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Special Issue Editor

Prof. Dr. Fridon Shubitidze

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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
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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.

You may choose our Joint Special Issue in Drones.

Prof. Fridon Shubitidze
Guest Editor

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Keywords

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

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Published Papers (3 papers)

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18 pages, 11538 KiB

Open AccessArticle

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 4 | Viewed by 2210

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 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 (

\frac{1}{R^{2}}

vs.

\frac{1}{R^{6}}

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

Open AccessArticle

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 22 | Viewed by 4065

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 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

Open AccessArticle

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 114 | Viewed by 8271

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 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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