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Recent Advances in Subsurface Sensing Technologies

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

Deadline for manuscript submissions: closed (30 June 2019) | Viewed by 42193

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Special Issue Editors

Prof. Dr. Fridon Shubitidze

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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
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Dr. Benjamin E. Barrowes

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Guest Editor

USACE-ERDC-CRREL, USA
Interests: electromagnetic sensing; sensors; UXO; LandMines; detection; classification

Special Issue Information

Dear Colleagues,

Detection and identification of subsurface and camouflage targets (unexploded ordnances, land mines, improvised explosive devices) and structures (tunnels, bunkers) in various mediums still remains one of most challenging problems worldwide. Recent developments in the remote sensing technologies, such as magnetic, electromagnetic induction, radar, optical, acoustic and seismic, together with robust signal processing approaches have provided enhanced detection and classification performances for finding hidden targets.

This Special Issue is open for all contributors in the field of recent developments in the remote sensing technologies (hardware and software) for detecting and identifications of targets of interests. We invite submissions of novel and original papers, case studies and reviews to this Special Issue that extend and advance our scientific/technical understanding of current state of the art remote sensing in areas that include, but are not limited to:

Magnetic, electromagnetic, acoustic, seismic and optical sensors and signal processing approaches for underwater targets detection and identification.
New and hybrid sensing modalities for landmines, improvised explosive devices and tunnels detection.
Forward and inverse modeling.
Classification techniques, such as linear classifiers, support vector machines, quadratic classifiers, neural networks;
Recent developments and integrations of remote sensing technologies with unmanned air systems.
Case studies during subsurface unexploded ordnances (UXO) detection and identification in land.

Prof. Fridon Shubitidze
Dr. Benjamin E. Barrowes
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

remote sensing
UXO
magnetics
electromagnetics induction
acoustic
land-mine
improvised explosive devices
underwater
classification

Published Papers (6 papers)

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17 pages, 9329 KiB

Open AccessArticle

High-Frequency Electromagnetic Induction (HFEMI) Sensor Results from IED Constituent Parts

by Benjamin Barrowes, Mikheil Prishvin, Guy Jutras and Fridon Shubitidze

Remote Sens. 2019, 11(20), 2355; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11202355 - 11 Oct 2019

Cited by 12 | Viewed by 4020

Abstract

The detection and classification of subsurface improvised explosive devices (IEDs) remains one of the most pressing military and civilian problems worldwide. These IEDs are often intentionally made with either very small metallic parts or less-conducting parts in order to evade low-frequency electromagnetic induction (EMI) sensors, or metal detectors, which operate at frequencies of 50 kHz or less. Recently, high-frequency electromagnetic induction (HFEMI), which extends the established EMI frequency range above 50 kHz to 20 MHz and bridges the gap between EMI and ground-penetrating radar frequencies, has shown promising results related to detecting and identifying IEDs. In this higher frequency range, less-conductive targets display signature inphase and quadrature responses similar to higher conducting targets in the LFEMI range. IED constituent parts, such as carbon rods, small pressure plates, conductivity voids, low metal content mines, and short wires respond to HFEMI but not to traditional low-frequency EMI (LFEMI). Results from recent testing over mock-ups of less-conductive IEDs or their components show distinctive HFEMI responses, suggesting that this new sensing realm could augment the detection and discrimination capability of established EMI technology. In this paper, we present results of using the HFEMI sensor over IED-like targets at the Fort AP Hill test site. We show that results agree with numerical modeling thus providing motives to incorporate sensing at these frequencies into traditional EMI and/or GPR-based sensors. Full article

(This article belongs to the Special Issue Recent Advances in Subsurface Sensing Technologies)

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32 pages, 9404 KiB

Open AccessArticle

Random Noise Suppression of Magnetic Resonance Sounding Data with Intensive Sampling Sparse Reconstruction and Kernel Regression Estimation

by Xiaokang Yao, Jianmin Zhang, Zhenyang Yu, Fa Zhao and Yong Sun

Remote Sens. 2019, 11(15), 1829; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11151829 - 05 Aug 2019

Cited by 4 | Viewed by 3226

Abstract

The magnetic resonance sounding (MRS) method is a non-invasive, efficient and advanced geophysical method for groundwater detection. However, the MRS signal received by the coil sensor is extremely susceptible to electromagnetic noise interference. In MRS data processing, random noise suppression of noisy MRS data is an important research aspect. We propose an approach for intensive sampling sparse reconstruction (ISSR) and kernel regression estimation (KRE) to suppress random noise. The approach is based on variable frequency sampling, numerical integration and statistical signal processing combined with kernel regression estimation. In order to realize the approach, we proposed three specific sparse reconstructions, namely rectangular sparse reconstruction, trapezoidal sparse reconstruction and Simpson sparse reconstruction. To solve the distortion of peaks and valleys after sparse reconstruction, we introduced the KRE to deal with the processed data by the ISSR. Further, the simulation and field experiments demonstrate that the ISSR-KRE approach is a feasible and effective way to suppress random noise. Besides, we find that rectangular sparse reconstruction and trapezoidal sparse reconstruction are superior to Simpson sparse reconstruction in terms of noise suppression effect, and sampling frequency is positively correlated with signal-to-noise improvement ratio (SNIR). In one case of field experiment, the standard deviation of noisy MRS data was reduced from 1200.80 nV to 570.01 nV by the ISSR-KRE approach. The proposed approach provides theoretical support for random noise suppression and contributes to the development of MRS instrument with low power consumption and high efficiency. In the future, we will integrate the approach into MRS instrument and attempt to utilize them to eliminate harmonic noise from power line. Full article

(This article belongs to the Special Issue Recent Advances in Subsurface Sensing Technologies)

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22 pages, 6487 KiB

Open AccessArticle

The Influence of Geostatistical Prior Modeling on the Solution of DCT-Based Bayesian Inversion: A Case Study from Chicken Creek Catchment

by Davood Moghadas and Jasper A. Vrugt

Remote Sens. 2019, 11(13), 1549; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11131549 - 29 Jun 2019

Cited by 12 | Viewed by 3490

Abstract

Low frequency loop-loop electromagnetic induction (EMI) is a widely-used geophysical measurement method to rapidly measure in situ the apparent electrical conductivity (ECa) of variably-saturated soils. Here, we couple Bayesian inversion of a quasi-two-dimensional electromagnetic (EM) model with image compression via the discrete cosine transform (DCT) for subsurface electrical conductivity (EC) imaging. The subsurface EC distributions are obtained from multi-configuration EMI data measured with a CMD-Explorer sensor along two transects in the Chicken Creek catchment (Brandenburg, Germany). Dipole-dipole electrical resistivity tomography (ERT) data are used to benchmark the inferred EC fields of both transects. We are especially concerned with the impact of the DCT truncation method on the accuracy and reliability of the inversely-estimated EC images. We contrast the results of two different truncation approaches for model parametrization. The first scenario considers an arbitrary selection of the dominant DCT coefficients and their prior distributions (a commonly-used approach), while the second methodology benefits from geostatistical simulation of the EMI data pseudosection. This study demonstrates that DCT truncation based on geostatistical simulations facilitates a robust selection of the dominant DCT coefficients and their prior ranges, resulting in more accurate subsurface EC imaging from multi-configuration EMI data. Results based on geostatistical prior modeling present an excellent agreement between the EMI- and ERT-derived EC fields of the Chicken Creek catchment. Full article

(This article belongs to the Special Issue Recent Advances in Subsurface Sensing Technologies)

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14 pages, 7560 KiB

Open AccessArticle

Detection and Identification of Remnant PFM-1 ‘Butterfly Mines’ with a UAV-Based Thermal-Imaging Protocol

by Alex Nikulin, Timothy S. De Smet, Jasper Baur, William D. Frazer and Jacob C. Abramowitz

Remote Sens. 2018, 10(11), 1672; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10111672 - 23 Oct 2018

Cited by 16 | Viewed by 22104

Abstract

Use of landmines as a weapon of unconventional warfare rapidly increased in armed conflicts of the last century and some estimates suggest that at least 100 million remain in place across post-conflict nations. Among munitions and explosives of concern (MECs), aerially deployed plastic anti-personnel mines are particularly challenging in terms of their detection and subsequent disposal. Detection and identification of MECs largely relies on the geophysical principles of magnetometry and electromagnetic-induction (EMI), which makes non-magnetic plastic MECs particularly difficult to detect and extremely dangerous to clear. In a recent study we demonstrated the potential of time-lapse thermal-imaging technology to detect unique thermal signatures associated with plastic MECs. Here, we present the results of a series of field trials demonstrating the viability of low-cost unmanned aerial vehicles (UAVs) equipped with infrared cameras to detect and identify the most notorious plastic landmines—the Soviet-era PFM-1 aerially deployed antipersonnel mine. We present results of an experiment simulating analysis of a full-scale ballistic PFM-1 minefield and demonstrate our ability to accurately detect and identify all elements associated with this type of deployment. We report significantly reduced time and equipment costs associated with the use of a UAV-mounted infrared system and anticipate its utility to both the scientific and non-governmental organization (NGO) community. Full article

(This article belongs to the Special Issue Recent Advances in Subsurface Sensing Technologies)

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17 pages, 8036 KiB

Open AccessArticle

Laboratory Measurements of Subsurface Spatial Moisture Content by Ground-Penetrating Radar (GPR) Diffraction and Reflection Imaging of Agricultural Soils

by Omer Shamir, Naftaly Goldshleger, Uri Basson and Moshe Reshef

Remote Sens. 2018, 10(10), 1667; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10101667 - 22 Oct 2018

Cited by 27 | Viewed by 4504

Abstract

Soil moisture content (SMC) down to the root zone is a major factor for the efficient cultivation of agricultural crops, especially in arid and semi-arid regions. Precise SMC can maximize crop yields (both quality and quantity), prevent crop damage, and decrease irrigation expenses and water waste, among other benefits. This study focuses on the subsurface spatial electromagnetic mapping of physical properties, mainly moisture content, using a ground-penetrating radar (GPR). In the laboratory, GPR measurements were carried out using an 800 MHz central-frequency antenna and conducted in soil boxes with loess soil type (calcic haploxeralf) from the northern Negev, hamra soil type (typic rhodoxeralf) from the Sharon coastal plain, and grumusol soil type (typic chromoxerets) from the Jezreel valley, Israel. These measurements enabled highly accurate, close-to-real-time evaluations of physical soil qualities (i.e., wave velocity and dielectric constant) connected to SMC. A mixture model based mainly on soil texture, porosity, and effective dielectric constant (permittivity) was developed to measure the subsurface spatial volumetric soil moisture content (VSMC) for a wide range of moisture contents. The analysis of the travel times for GPR reflection and diffraction waves enabled calculating electromagnetic velocities, effective dielectric constants, and spatial SMC under laboratory conditions, where the required penetration depth is low (root zone). The average VSMC was determined with an average accuracy of ±1.5% and was correlated to a standard oven-drying method, making this spatial method useful for agricultural practice and for the design of irrigation plans for different interfaces. Full article

(This article belongs to the Special Issue Recent Advances in Subsurface Sensing Technologies)

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17 pages, 5977 KiB

Open AccessTechnical Note

Compensation of Dispersion in Sinuous Antennas for Polarimetric Ground Penetrating Radar Applications

by Dylan A. Crocker and Waymond R. Scott, Jr.

Remote Sens. 2019, 11(16), 1937; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11161937 - 19 Aug 2019

Cited by 6 | Viewed by 3856

Abstract

In order to improve the accuracy of subsurface target classification with ground penetrating radar (GPR) systems, it is desired to transmit and receive ultra-wide band pulses with varying combinations of polarization (a technique referred to as polarimetry). The sinuous antenna exhibits such desirable properties as ultra-wide bandwidth, polarization diversity, and low-profile form factor, making it an excellent candidate for the radiating element of such systems. However, sinuous antennas are dispersive since the active region moves with frequency along the structure, resulting in the distortion of radiated pulses. This distortion may be compensated in signal processing with accurately simulated or measured antenna phase information. However, in a practical GPR, the antenna performance may deviate from that simulated, accurate measurements may be impractical, and/or the dielectric loading of the environment may cause deviations. In such cases, it may be desirable to employ a simple dispersion model based on antenna design parameters which may be optimized in situ. This paper explores the dispersive properties of the sinuous antenna and presents a simple, adjustable, model that may be used to correct dispersed pulses. The dispersion model is successfully applied to both simulated and measured scenarios, thereby enabling the use of sinuous antennas in polarimetric GPR applications. Full article

(This article belongs to the Special Issue Recent Advances in Subsurface Sensing Technologies)

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