{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T15:10:08Z","timestamp":1767453008455,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,31]],"date-time":"2022-08-31T00:00:00Z","timestamp":1661904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61871425","61861011","GuikeAA21077008","21-JKCF-55"],"award-info":[{"award-number":["61871425","61861011","GuikeAA21077008","21-JKCF-55"]}]},{"name":"Guangxi special fund project for innovation-driven development","award":["61871425","61861011","GuikeAA21077008","21-JKCF-55"],"award-info":[{"award-number":["61871425","61861011","GuikeAA21077008","21-JKCF-55"]}]},{"name":"Shanxi Transportation Technology R&amp;D Co. Ltd.","award":["61871425","61861011","GuikeAA21077008","21-JKCF-55"],"award-info":[{"award-number":["61871425","61861011","GuikeAA21077008","21-JKCF-55"]}]},{"name":"Innovation Development Plan Unveiling Project","award":["61871425","61861011","GuikeAA21077008","21-JKCF-55"],"award-info":[{"award-number":["61871425","61861011","GuikeAA21077008","21-JKCF-55"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Correctly estimating the relative permittivity of buried targets is crucial for accurately determining the target type, geometric size, and reconstruction of shallow surface geological structures. In order to effectively identify the dielectric properties of buried targets, on the basis of extracting the feature information of B-SCAN images, we propose an inversion method based on a deep neural network (DNN) to estimate the relative permittivity of targets. We first take the physical mechanism of ground-penetrating radar (GPR), working in the reflection measurement mode as the constrain condition, and then design a convolutional neural network (CNN) to extract the feature hyperbola of the underground target, which is used to calculate the buried depth of the target and the relative permittivity of the background medium. We further build a regression network and train the network model with the labeled sample set to estimate the relative permittivity of the target. Tests were carried out on the GPR simulation dataset and the field dataset of underground rainwater pipelines, respectively. The results show that the inversion method has high accuracy in estimating the relative permittivity of the target.<\/jats:p>","DOI":"10.3390\/rs14174293","type":"journal-article","created":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T03:55:38Z","timestamp":1662004538000},"page":"4293","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Deep-Learning-Based Method for Estimating Permittivity of Ground-Penetrating Radar Targets"],"prefix":"10.3390","volume":"14","author":[{"given":"Hui","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China"},{"name":"School of Artificial Intelligence, Hezhou University, Hezhou 542899, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9513-8648","authenticated-orcid":false,"given":"Shan","family":"Ouyang","sequence":"additional","affiliation":[{"name":"School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China"}]},{"given":"Qinghua","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China"}]},{"given":"Kefei","family":"Liao","sequence":"additional","affiliation":[{"name":"School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China"}]},{"given":"Lijun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Shanxi Transportation Technology R&D Co., Ltd., Taiyuan 030032, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shen, R., Zhao, Y., Hu, S., Li, B., and Bi, W. 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