气候变化领域集成服务门户(CCPortal): Deep Learning-Based Suppression of Strong Noise in GPR Data for Railway Subgrade Detection

CCPortal

DOI	10.1109/TGRS.2024.3400309
	Deep Learning-Based Suppression of Strong Noise in GPR Data for Railway Subgrade Detection
	Liu, Zhihang; Xiao, Jianping; Shen, Ruijie; Liu, Jianxin; Guo, Zhenwei
发表日期	2024
ISSN	0196-2892
EISSN	1558-0644
起始页码	62
卷号	62
英文摘要	Ground-penetrating radar (GPR) is a nondestructive near-surface geophysical detection method, which is often used to locate subgrade diseases in railway subgrade detection. However, the strong noise reflected by the sleeper will obscure the effective information of the subgrade disease. In order to suppress this strong noise, the traditional processing method is generally filtering, which is dependent on expert experience. We propose a method of railway sleeper interference suppression based on UNet. We use datasets based on real railway subgrade structures to train UNet. Each dataset consists of a pair of GPR data with sleeper interference and GPR data without sleeper interference. The experimental results of simulation data show that the SSIM value between the data processed by UNet and the data without sleeper interference exceeds 0.99. We test our approach on field data collected on the Qinghai-Tibet Railway. Compared with traditional filtering methods, UNet does not rely on expert experience and is more effective. The research shows that our method can effectively suppress the sleeper strong interference in GPR data and improve the signal-to-noise ratio. We expect that our method can promote the development of railway subgrade detection.
英文关键词	Rail transportation; Interference; Feature extraction; Diseases; Electronic ballasts; Atmospheric modeling; Deep learning; ground-penetrating radar (GPR); noise suppression; railway subgrade detection; UNet
语种	英语
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001236662700018
来源期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/296307
作者单位	Central South University
推荐引用方式 GB/T 7714	Liu, Zhihang,Xiao, Jianping,Shen, Ruijie,et al. Deep Learning-Based Suppression of Strong Noise in GPR Data for Railway Subgrade Detection[J],2024,62.
APA	Liu, Zhihang,Xiao, Jianping,Shen, Ruijie,Liu, Jianxin,&Guo, Zhenwei.(2024).Deep Learning-Based Suppression of Strong Noise in GPR Data for Railway Subgrade Detection.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,62.
MLA	Liu, Zhihang,et al."Deep Learning-Based Suppression of Strong Noise in GPR Data for Railway Subgrade Detection".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62(2024).