Remote Sensing

Research

23 pages, 4373 KiB

Open AccessArticle

Enhancing Image Alignment in Time-Lapse-Ground-Penetrating Radar through Dynamic Time Warping

by Jiahao Wen, Tianbao Huang, Xihong Cui, Yaling Zhang, Jinfeng Shi, Yanjia Jiang, Xiangjie Li and Li Guo

Remote Sens. 2024, 16(6), 1040; https://doi.org/10.3390/rs16061040 - 15 Mar 2024

Viewed by 555

Abstract

Ground-penetrating radar (GPR) is a rapid and non-destructive geophysical technique widely employed to detect and quantify subsurface structures and characteristics. Its capability for time lapse (TL) detection provides essential insights into subsurface hydrological dynamics, including lateral flow and soil water distribution. However, during [...] Read more.

Ground-penetrating radar (GPR) is a rapid and non-destructive geophysical technique widely employed to detect and quantify subsurface structures and characteristics. Its capability for time lapse (TL) detection provides essential insights into subsurface hydrological dynamics, including lateral flow and soil water distribution. However, during TL-GPR surveys, field conditions often create discrepancies in surface geometry, which introduces mismatches across sequential TL-GPR images. These discrepancies may generate spurious signal variations that impede the accurate interpretation of TL-GPR data when assessing subsurface hydrological processes. In responding to this issue, this study introduces a TL-GPR image alignment method by employing the dynamic time warping (DTW) algorithm. The purpose of the proposed method, namely TLIAM–DTW, is to correct for geometric mismatch in TL-GPR images collected from the identical survey line in the field. We validated the efficacy of the TLIAM–DTW method using both synthetic data from gprMax V3.0 simulations and actual field data collected from a hilly, forested area post-infiltration experiment. Analyses of the aligned TL-GPR images revealed that the TLIAM–DTW method effectively eliminates the influence of geometric mismatch while preserving the integrity of signal variations due to actual subsurface hydrological processes. Quantitative assessments of the proposed methods, measured by mean absolute error (MAE) and root mean square error (RMSE), showed significant improvements. After performing the TLIAM–DTW method, the MAE and RMSE between processed TL-GPR images and background images were reduced by 96% and 78%, respectively, in simple simulation scenarios; in more complex simulations, MAE declined by 27–31% and RMSE by 17–43%. Field data yielded reductions in MAE and RMSE of >82% and 69%, respectively. With these substantial improvements, the processed TL-GPR images successfully depict the spatial and temporal transitions associated with subsurface lateral flows, thereby enhancing the accuracy of monitoring subsurface hydrological processes under field conditions. Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR) Applications in Earth, Moon and Planetary Exploration)

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28 pages, 18037 KiB

Open AccessArticle

Environmental Influences on the Detection of Buried Objects with a Ground-Penetrating Radar

by Bernd Arendt, Michael Schneider, Winfried Mayer and Thomas Walter

Remote Sens. 2024, 16(6), 1011; https://doi.org/10.3390/rs16061011 - 13 Mar 2024

Viewed by 579

Abstract

A tremendous number of landmines has been buried during the last decade. In recent years, various autonomous platforms equipped with ground-penetrating radars (GPRs) have been proposed for the detection of landmines. These systems have already demonstrated their performance in controlled environments with known [...] Read more.

A tremendous number of landmines has been buried during the last decade. In recent years, various autonomous platforms equipped with ground-penetrating radars (GPRs) have been proposed for the detection of landmines. These systems have already demonstrated their performance in controlled environments with known ground truth. However, it has been observed that the influence of surface conditions in the form of vegetation and roughness as well as soil moisture content significantly reduce the detection probability. The influence of these individual factors on a ground-offset GPR is presented and discussed in this work. Each of these factors significantly degrades the backscattered signal. With increasing soil moisture, the signal gets attenuated more strongly; however, the signature is maintained in the phase of the C-Scans. An increase in surface roughness deteriorates the target pattern making it difficult to detect buried objects unambiguously. Vegetation, especially with irregular leaf structures, can appear as a ghost target and scatter the electromagnetic waves. In most cases, the target is easier to detect in the phase of the B- or C-Scan. Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR) Applications in Earth, Moon and Planetary Exploration)

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16 pages, 2904 KiB

Open AccessArticle

GAN-Based Inversion of Crosshole GPR Data to Characterize Subsurface Structures

by Donghao Zhang, Zhengzheng Wang, Hui Qin, Tiesuo Geng and Shengshan Pan

Remote Sens. 2023, 15(14), 3650; https://doi.org/10.3390/rs15143650 - 21 Jul 2023

Cited by 1 | Viewed by 1005

Abstract

The crosshole ground-penetrating radar (GPR) technique is widely used to characterize subsurface structures, yet the interpretation of crosshole GPR data involves solving non-linear and ill-posed inverse problems. In this work, we developed a generative adversarial network (GAN)-based inversion framework to translate crosshole GPR [...] Read more.

The crosshole ground-penetrating radar (GPR) technique is widely used to characterize subsurface structures, yet the interpretation of crosshole GPR data involves solving non-linear and ill-posed inverse problems. In this work, we developed a generative adversarial network (GAN)-based inversion framework to translate crosshole GPR images to their corresponding 2D defect reconstruction images automatically. This approach uses fully connected layers to extract global features from crosshole GPR images and employs a series of cascaded U-Net structures to produce high-resolution defect reconstruction results. The feasibility of the proposed framework was demonstrated on a synthetic crosshole GPR dataset created with the finite-difference time-domain (FDTD) method and real-world data from a field experiment. Our inversion network obtained recognition accuracy of 91.36%, structural similarity index measure (SSIM) of 0.93, and RAscore of 91.77 on the test dataset. Furthermore, comparisons with ray-based tomography and full-waveform inversion (FWI) suggest that the proposed method provides a good balance between inversion accuracy and efficiency and has the best generalization when inverting actual measured crosshole GPR data. Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR) Applications in Earth, Moon and Planetary Exploration)

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18 pages, 12366 KiB

Open AccessArticle

Estimation of the Soil Water Content Using the Early Time Signal of Ground-Penetrating Radar in Heterogeneous Soil

by Qi Lu, Kexin Liu, Zhaofa Zeng, Sixin Liu, Risheng Li, Longfei Xia, Shilong Guo and Zhilian Li

Remote Sens. 2023, 15(12), 3026; https://doi.org/10.3390/rs15123026 - 9 Jun 2023

Cited by 1 | Viewed by 988

Abstract

Ground-penetrating radar (GPR) is an important tool for measuring soil water content (SWC) at the field scale. The amplitude analysis of the early time signal (ETS) of GPR may provide a rapid way to estimate SWC. By assuming a homogeneous medium, various studies [...] Read more.

Ground-penetrating radar (GPR) is an important tool for measuring soil water content (SWC) at the field scale. The amplitude analysis of the early time signal (ETS) of GPR may provide a rapid way to estimate SWC. By assuming a homogeneous medium, various studies have been conducted on the relationship between the amplitude of ETS and the topsoil layer’s electromagnetic parameters (dielectric permittivity and conductivity) through numerical simulations, laboratory experiments, and field experiments. Soil is a typical inhomogeneous medium, and soil cultivation is a factor affecting its heterogeneity. In this context, we discuss the ability of the amplitude of ETS to estimate the water content of heterogeneous soil. First, we establish a multi-scale stochastic medium model with the inhomogeneous distribution of dielectric permittivity and conductivity and simulate the GPR response by the finite-difference time-domain (FDTD) method to observe the influence of medium heterogeneity on the GPR response. The heterogeneity of the soil models is evaluated by a geostatistical analysis described by two parameters, correlation length and variability. Then, we analyze the relationship between variability and the average envelope amplitude (AEA) of ETS. A strong soil heterogeneity increases the error of the AEA method in estimating SWC. Finally, the AEA method is used to estimate the SWC of two adjacent fields with different heterogeneities, which were caused by different cultivation methods. The results of the numerical simulation and field experiment indicate that the soil heterogeneity can have an impact on the estimation of SWC using EST, with an error lower than 3% within a depth range of 1/2 λ to λ (wavelength). This suggests that the EST of GPR can be applied to soil layers with relatively large lateral changes in water content. Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR) Applications in Earth, Moon and Planetary Exploration)

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16 pages, 5364 KiB

Open AccessArticle

Spatial Variability of Active Layer Thickness along the Qinghai–Tibet Engineering Corridor Resolved Using Ground-Penetrating Radar

by Shichao Jia, Tingjun Zhang, Jiansheng Hao, Chaoyue Li, Roger Michaelides, Wanwan Shao, Sihao Wei, Kun Wang and Chengyan Fan

Remote Sens. 2022, 14(21), 5606; https://doi.org/10.3390/rs14215606 - 7 Nov 2022

Cited by 2 | Viewed by 1706

Abstract

Active layer thickness (ALT) is a sensitive indicator of response to climate change. ALT has important influence on various aspects of the regional environment such as hydrological processes and vegetation. In this study, 57 ground-penetrating radar (GPR) sections were surveyed along the Qinghai–Tibet [...] Read more.

Active layer thickness (ALT) is a sensitive indicator of response to climate change. ALT has important influence on various aspects of the regional environment such as hydrological processes and vegetation. In this study, 57 ground-penetrating radar (GPR) sections were surveyed along the Qinghai–Tibet Engineering Corridor (QTEC) during 2018–2021, covering a total length of 58.5 km. The suitability of GPR-derived ALT was evaluated using in situ measurements and reference datasets, for which the bias and root mean square error were approximately −0.16 and 0.43 m, respectively. The GPR results show that the QTEC ALT was in the range of 1.25–6.70 m (mean: 2.49 ± 0.57 m). Observed ALT demonstrated pronounced spatial variability at both regional and fine scales. We developed a statistical estimation model that explicitly considers the soil thermal regime (i.e., ground thawing index, TI_g), soil properties, and vegetation. This model was found suitable for simulating ALT over the QTEC, and it could explain 52% (R² = 0.52) of ALT variability. The statistical model shows that a difference of 10 °C.d in

\sqrt{T I_{g}}

is equivalent to a change of 0.67 m in ALT, and an increase of 0.1 in the normalized difference vegetation index (NDVI) is equivalent to a decrease of 0.23 m in ALT. The fine-scale (<1 km) variation in ALT could account for 77.6% of the regional-scale (approximately 550 km) variation. These results provide a timely ALT benchmark along the QTEC, which can inform the construction and maintenance of engineering facilities along the QTEC. Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR) Applications in Earth, Moon and Planetary Exploration)

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Review

Jump to: Research, Other

0 pages, 24850 KiB

Open AccessReview

Radar Observation of the Lava Tubes on the Moon and Mars

by Xiaohang Qiu and Chunyu Ding

Remote Sens. 2023, 15(11), 2850; https://doi.org/10.3390/rs15112850 - 30 May 2023

Cited by 8 | Viewed by 2872

Abstract

The detection of lava tubes beneath the surfaces of the Moon and Mars has been a popular research topic and challenge in planetary radar observation. In recent years, the Moon–based ground penetrating radar (GPR) carried by the Chinese Chang’e–3/–4 mission, the RIMFAX radar [...] Read more.

The detection of lava tubes beneath the surfaces of the Moon and Mars has been a popular research topic and challenge in planetary radar observation. In recent years, the Moon–based ground penetrating radar (GPR) carried by the Chinese Chang’e–3/–4 mission, the RIMFAX radar carried by the Mars mission Perseverance, and the RoSPR radar and MOSIR radar carried by China’s Tianwen–1 orbiter have extensively promoted the exploration of the underground space of extraterrestrial bodies, which is crucial for the future utilization and development of these spaces. This paper expounds on the principles, methods, and detection results of using GPR to detect lava tubes on the Moon and Mars. First, lava tubes’ formation mechanism and morphological characteristics are outlined, followed by an introduction to GPR’s working principles and classification. The advantages, disadvantages, and prospects of different types of radar in detecting the lava tubes are analyzed. Finally, the distribution of lava tubes on the Moon and Mars is briefly summarized, and the potential utilization of lava tubes is discussed. We believe that the GPR technique is an effective geophysical method for exploring the underground structures of the Moon and Mars, and the lava tubes beneath the surface of extraterrestrial bodies can provide important references for selecting future Moon and Mars bases. Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR) Applications in Earth, Moon and Planetary Exploration)

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13 pages, 18243 KiB

Open AccessTechnical Note

The LPR Instantaneous Centroid Frequency Attribute Based on the 1D Higher-Order Differential Energy Operator

by Xuebing Zhang, Zhengchun Song, Bonan Li, Xuan Feng, Jiangang Zhou, Yipeng Yu and Xin Hu

Remote Sens. 2023, 15(22), 5305; https://doi.org/10.3390/rs15225305 - 9 Nov 2023

Viewed by 690

Abstract

In ground-penetrating radar (GPR) or lunar-penetrating radar (LPR) interpretation, instantaneous attributes (e.g., instantaneous energy and instantaneous frequency) are often utilized for attribute analysis, and they can also be integrated into a new attribute, i.e., the instantaneous centroid frequency. Traditionally, the estimation of instantaneous [...] Read more.

In ground-penetrating radar (GPR) or lunar-penetrating radar (LPR) interpretation, instantaneous attributes (e.g., instantaneous energy and instantaneous frequency) are often utilized for attribute analysis, and they can also be integrated into a new attribute, i.e., the instantaneous centroid frequency. Traditionally, the estimation of instantaneous attributes calls for complex trace analysis or energy operator schemes (e.g., the Teager–Kaiser energy operator, TKEO). In this work, we introduce the 1D higher-order differential energy operator (1D-HODEO) to track instantaneous attributes with better localization. In collocation with the mode decomposition algorithms, the 1D-HODEO performs along each A-scan on the decomposed mode slices to form the final profile of instantaneous centroid frequency by using the piece-wise correlation coefficients. Both a numerical model for simulating two-layer lunar regolith and the LPR Yutu-2 data show that the proposed instantaneous centroid frequency profile on the 1D-HODEO has better resolution, in comparison with that of TKEO and the traditional time-varying centroid frequency. In this work, we present a new approach for extracting instantaneous centroid frequency attributes which provides more comprehensive information in lunar stratigraphic interpretation and LPR attribute analysis. Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR) Applications in Earth, Moon and Planetary Exploration)

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17 pages, 34224 KiB

Open AccessTechnical Note

Directional and High-Gain Ultra-Wideband Bow-Tie Antenna for Ground-Penetrating Radar Applications

by Shuai Pi, Tianhao Wang and Jun Lin

Remote Sens. 2023, 15(14), 3522; https://doi.org/10.3390/rs15143522 - 12 Jul 2023

Cited by 1 | Viewed by 2216

Abstract

Bow-tie antennas are utilized extensively in ground-penetrating radar (GPR) systems. In order to achieve sufficient penetration depth and resolution, the bow-tie antennas for GPR applications require low operating frequency, high gain, and excellent broadband. A novel ultra-wideband (UWB) bow-tie antenna with gain enhancement [...] Read more.

Bow-tie antennas are utilized extensively in ground-penetrating radar (GPR) systems. In order to achieve sufficient penetration depth and resolution, the bow-tie antennas for GPR applications require low operating frequency, high gain, and excellent broadband. A novel ultra-wideband (UWB) bow-tie antenna with gain enhancement for GPR applications is proposed in this paper. First, a UWB bow-tie antenna with resistive loading is designed. The metal reflector and metamaterial loading make the bow-tie antenna directional, and loading the same metamaterial on the front side of the antenna further improves directional gain. After testing, the lowest frequency of the fabricated antenna is 317 MHz, the relative bandwidth is 98.6%, the peak gain in the frequency range is 9.3 dBi, and the size is only 0.38 λ at the lowest frequency. The proposed compact antenna takes both gain and bandwidth into consideration. Finally, in order to further verify the effectiveness of the proposed antenna in the GPR system, a stepped frequency continuous wave ground-penetrating radar (SFCW-GPR) system was built. The experimental results show that the designed antenna is suitable for the GPR system of deep penetration and high-resolution detection, which is beneficial to the imaging of underground structures. Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR) Applications in Earth, Moon and Planetary Exploration)

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