Deep Learning-Based Suppression of Strong Noise in GPR Data for Railway Subgrade Detection-Reference-Cited by-同舟云学术

Deep Learning-Based Suppression of Strong Noise in GPR Data for Railway Subgrade Detection

Published:2024 Issue: Volume:62 Page:1-9
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Liu Zhihang¹^ORCID,Xiao Jianping¹^ORCID,Shen Ruijie¹^ORCID,Liu Jianxin¹^ORCID,Guo Zhenwei¹^ORCID

Affiliation:

1. School of Geo-sciences and Info-Physics, Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring Ministry of Education, Central South University, Changsha, China

Funder

National Natural Science Foundation of China

Fundamental Research Funds for Central Universities of the Central South University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10530102.pdf?arnumber=10530102

Reference35 articles.

1. Combined CNN and RNN Neural Networks for GPR Detection of Railway Subgrade Diseases

2. A Robust and Scalable Multigrid Solver for 3-D Low-Frequency Electromagnetic Diffusion Problems

3. Application of Convolutional and Recurrent Neural Networks for Buried Threat Detection Using Ground Penetrating Radar Data

4. Deep learning based automated analysis of archaeo‐geophysical images

5. An algorithm for automatic localization and detection of rebars from GPR data of concrete bridge decks

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