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New Robust Sparse Convolutional Coding Inversion Algorithm for Ground Penetrating Radar Images
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DOI：10.1109/TGRS.2023.3268477.png)
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摘要
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In this article, we propose two algorithms to enhance the interpretability of the hyperbola in B-scans obtained with a ground penetrating radar (GPR). These hyperbolas are the responses of buried objects or cavities. To correctly detect and classify them, denoising is typically necessary for GPR images as the signal-to-noise ratio (SNR) is low, and the various interfaces naturally present in the Earth have a strong response. Both algorithms are based on a sparse convolutional coding model plus a low-rank component. It is solved through an alternating direction method of multipliers (ADMM) framework. In order to take into account the presence of outliers and the artifacts caused by the acquisition, the second algorithm is based on the Huber norm instead of the classic L-2 -norm. These algorithms are tested on a real dataset labeled by geophysicists. The results show the denoising efficiency of this approach, and in particular the robustness of the second algorithm.
Keyword:
Dictionaries
Radar
Signal to noise ratio
Radar imaging
Signal processing algorithms
Shape
Radar antennas
Convolutive model
ground penetrating radar (GPR)
robust methods
sparse inversion
期刊
IF:
8.6
论文数:
2.1W
被引数:
10.7W
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引用论文
An overview of ground-penetrating radar signal processing techniques for road inspections.png)
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