An Attention U-Net-Based Improved Clutter Suppression in GPR Images

The existence of strong background clutter often masks the desired target response, and thereby significantly affects the ground-penetrating radar (GPR) target detection. This effect is even more pronounced for rough terrain and shallow buried targets. Therefore, it is essential to eliminate the clu...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2024, Vol.73, p.1-11
Main Authors: Panda, Swarna Laxmi, Sahoo, Upendra Kumar, Maiti, Subrata, Sasmal, Pradipta
Format: Article
Language:English
Subjects:
Attention U-Net
channel attention module (CAM)
Clutter
clutter removal
Decoding
Deep learning
Feature extraction
Ground penetrating radar
ground radar (GPR)
Media
Modules
Radar detection
Rough terrain
spatial attention module (SAM)
Surface roughness
Target detection
Target masking
Training
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Summary:The existence of strong background clutter often masks the desired target response, and thereby significantly affects the ground-penetrating radar (GPR) target detection. This effect is even more pronounced for rough terrain and shallow buried targets. Therefore, it is essential to eliminate the clutter to facilitate the target detection. In this article, a deep-learning-based attention U-Net model is proposed for clutter removal of GPR data. This technique integrates a channel attention module (CAM) and a spatial attention module (SAM) with a U-Net architecture to enhance the clutter removal performance. The proposed model implicitly learns to suppress irrelevant clutters while emphasizing the desired target. The effectiveness of the proposed clutter removal approach is validated on synthetic and measured data through visual inspection and quantitative evaluation.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2024.3378267