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DL-Based Clutter Removal in Migrated GPR Data for Detection of Buried Target
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AI
DOI：10.1109/LGRS.2021.3089246.png)
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Abstract
En 中文
As a nondestructive and nonintrusive geophysical electromagnetic technology, ground-penetrating radar (GPR) has been widely applied to subsurface target detection, such as landmine detection, pipeline detection, and underground cavity detection. The target response received by the GPR system is generally contaminated by clutter, which greatly affects the detection performance of the buried targets. In this letter, a novel clutter removal method combining migration and dictionary learning (DL) is presented. First, the proposed method applies the frequency-wavenumber (F-K) migration to the received GPR B-scan data. Then, since the focused target response and the clutter in the migrated GPR B-scan data present different morphological components, DL can be applied to the migrated GPR B-scan data to separate the focused target response (point-shaped structure) from the clutter (horizontal strip-shaped structure). Both numerical simulated data and experimental data collected by a real GPR system are used to evaluate the performance of the proposed clutter removal method. The experimental results demonstrate the effectiveness of the proposed clutter removal method under irregular clutter conditions, which improves the detection ability of the buried targets.
Keywords:
Clutter
Dictionaries
Pipelines
Soil
Data models
Sparse matrices
Aluminum
Clutter removal
dictionary learning (DL)
ground-penetrating radar (GPR)
sparse representation
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