Rough ground surface clutter removal in air-coupled ground penetrating radar data using low-rank and sparse representation

Zhang, Yu; Burns, Dylan; Orfeo, Dan; Huston, Dryver R.; Xia, Tian

doi:10.1117/12.2261355

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…Hence, RPCA has been applied to identify such defects in sequences of ultrasonic sector images [146]. RPCA also has been implemented on ground-penetrating radar results as an image enhancement technique [147].…”

Section: Robust Principal Component Analysismentioning

confidence: 99%

High-dimensional data analytics in structural health monitoring and non-destructive evaluation: a review paper

Momeni

Ebrahimkhanlou

2022

Smart Mater. Struct.

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This paper aims to review high-dimensional data analytic methods for structural health monitoring (SHM) and non-destructive evaluation (NDE) applications. High dimensional data is a type of data in which the number of features for each observation is much larger than the number of all observations. High dimensional data may violate assumptions of the classic methods for statistical modeling and data analysis. Then, classic statistical modeling will no longer be applicable. High dimensional data analytics (HDDA) methods were developed to overcome this challenge and analyze these types of data. In the field of SHM/NDE, there are several sources of high-dimensionality. Examples include a large number of data points in continuous waves/signals or high-resolution images/videos. HDDA methods are used as a dimension reduction tool to preprocess data for further analysis, or they are directly implemented for damage detection and localization. This paper reviews six HDDA methods as well as existing and potential applications in SHM/NDE. Particularly, this paper discusses the vast range of implemented SHM/NDE applications from crack detection to data missing data imputation. Furthermore, experimental and simulated datasets have been used to show the application of HDDA methods as hands on examples. It is shown that the potential of HDDA for SHM/NDE studies is significantly more than the existing studies in the literature, and these methods can be used as a powerful tool that provides vast opportunities in SHM/NDE.

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Section: Robust Principal Component Analysismentioning

confidence: 99%

High-dimensional data analytics in structural health monitoring and non-destructive evaluation: a review paper

Momeni

Ebrahimkhanlou

2022

Smart Mater. Struct.

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“…Robust principal component analysis (RPCA) [21], which decomposes the GPR image into a low-rank clutter matrix and a sparse target matrix, has demonstrated its superiority to the conventional subspace-based methods in GPR clutter removal [22,23]. Motivated by its success, various low-rank and sparse decomposition (LRSD) methods are successively proposed [24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

GPR Clutter Removal Based on Weighted Nuclear Norm Minimization for Nonparallel Cases

Liu

Song

et al. 2023

Sensors

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Ground-penetrating radar (GPR) is an effective geophysical electromagnetic method for underground target detection. However, the target response is usually overwhelmed by strong clutter, thus damaging the detection performance. To account for the nonparallel case of the antennas and the ground surface, a novel GPR clutter-removal method based on weighted nuclear norm minimization (WNNM) is proposed, which decomposes the B-scan image into a low-rank clutter matrix and a sparse target matrix by using a non-convex weighted nuclear norm and assigning different weights to different singular values. The WNNM method’s performance is evaluated using both numerical simulations and experiments with real GPR systems. Comparative analysis with the commonly used state-of-the-art clutter removal methods is also conducted in terms of the peak signal-to-noise ratio (PSNR) and the improvement factor (IF). The visualization and quantitative results demonstrate that the proposed method outperforms the others in the nonparallel case. Moreover, it is about five times faster than the RPCA, which is beneficial for practical applications.

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Ground Penetrating Radar Signal Processing Method Research for Hidden Cable Accurate Detection

He,

Meng,

Lin

et al. 2023

2023 16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

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Rough ground surface clutter removal in air-coupled ground penetrating radar data using low-rank and sparse representation

Cited by 5 publications

References 12 publications

High-dimensional data analytics in structural health monitoring and non-destructive evaluation: a review paper

High-dimensional data analytics in structural health monitoring and non-destructive evaluation: a review paper

GPR Clutter Removal Based on Weighted Nuclear Norm Minimization for Nonparallel Cases

Ground Penetrating Radar Signal Processing Method Research for Hidden Cable Accurate Detection

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