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

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...

Full description

Bibliographic Details
Main Authors: Jiahao Wen, Tianbao Huang, Xihong Cui, Yaling Zhang, Jinfeng Shi, Yanjia Jiang, Xiangjie Li, Li Guo
Format: Article
Language:English
Published: MDPI AG 2024-03-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/16/6/1040
_version_ 1797239455490244608
author Jiahao Wen
Tianbao Huang
Xihong Cui
Yaling Zhang
Jinfeng Shi
Yanjia Jiang
Xiangjie Li
Li Guo
author_facet Jiahao Wen
Tianbao Huang
Xihong Cui
Yaling Zhang
Jinfeng Shi
Yanjia Jiang
Xiangjie Li
Li Guo
author_sort Jiahao Wen
collection DOAJ
description 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.
first_indexed 2024-04-24T17:51:49Z
format Article
id doaj.art-80e142a2aa144bcf91413175c96af342
institution Directory Open Access Journal
issn 2072-4292
language English
last_indexed 2024-04-24T17:51:49Z
publishDate 2024-03-01
publisher MDPI AG
record_format Article
series Remote Sensing
spelling doaj.art-80e142a2aa144bcf91413175c96af3422024-03-27T14:02:41ZengMDPI AGRemote Sensing2072-42922024-03-01166104010.3390/rs16061040Enhancing Image Alignment in Time-Lapse-Ground-Penetrating Radar through Dynamic Time WarpingJiahao Wen0Tianbao Huang1Xihong Cui2Yaling Zhang3Jinfeng Shi4Yanjia Jiang5Xiangjie Li6Li Guo7State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaCollege of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi 832000, ChinaState Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaGround-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.https://www.mdpi.com/2072-4292/16/6/1040GPRgeometric mismatchdynamic time warping (DTW)subsurface flowforward simulationfield condition
spellingShingle Jiahao Wen
Tianbao Huang
Xihong Cui
Yaling Zhang
Jinfeng Shi
Yanjia Jiang
Xiangjie Li
Li Guo
Enhancing Image Alignment in Time-Lapse-Ground-Penetrating Radar through Dynamic Time Warping
Remote Sensing
GPR
geometric mismatch
dynamic time warping (DTW)
subsurface flow
forward simulation
field condition
title Enhancing Image Alignment in Time-Lapse-Ground-Penetrating Radar through Dynamic Time Warping
title_full Enhancing Image Alignment in Time-Lapse-Ground-Penetrating Radar through Dynamic Time Warping
title_fullStr Enhancing Image Alignment in Time-Lapse-Ground-Penetrating Radar through Dynamic Time Warping
title_full_unstemmed Enhancing Image Alignment in Time-Lapse-Ground-Penetrating Radar through Dynamic Time Warping
title_short Enhancing Image Alignment in Time-Lapse-Ground-Penetrating Radar through Dynamic Time Warping
title_sort enhancing image alignment in time lapse ground penetrating radar through dynamic time warping
topic GPR
geometric mismatch
dynamic time warping (DTW)
subsurface flow
forward simulation
field condition
url https://www.mdpi.com/2072-4292/16/6/1040
work_keys_str_mv AT jiahaowen enhancingimagealignmentintimelapsegroundpenetratingradarthroughdynamictimewarping
AT tianbaohuang enhancingimagealignmentintimelapsegroundpenetratingradarthroughdynamictimewarping
AT xihongcui enhancingimagealignmentintimelapsegroundpenetratingradarthroughdynamictimewarping
AT yalingzhang enhancingimagealignmentintimelapsegroundpenetratingradarthroughdynamictimewarping
AT jinfengshi enhancingimagealignmentintimelapsegroundpenetratingradarthroughdynamictimewarping
AT yanjiajiang enhancingimagealignmentintimelapsegroundpenetratingradarthroughdynamictimewarping
AT xiangjieli enhancingimagealignmentintimelapsegroundpenetratingradarthroughdynamictimewarping
AT liguo enhancingimagealignmentintimelapsegroundpenetratingradarthroughdynamictimewarping