Learning From Clutter: An Unsupervised Learning-Based Clutter Removal Scheme for GPR B-Scans

Ground-penetrating radar (GPR) data are often contaminated by hardware and environmental clutter, which significantly affects the accuracy and reliability of target response identification. Existing supervised deep learning techniques for removing clutter in GPR data require generating a large set o...

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Autori principali:	Qiqi Dai, Yee Hui Lee, Hai-Han Sun, Jiwei Qian, Mohamed Lokman Mohd Yusof, Daryl Lee, Abdulkadir C. Yucel
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	IEEE 2024-01-01
Serie:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Soggetti:	Clutter removal contrastive learning data augmentation diffusion model generative adversarial network ground-penetrating radar (GPR)
Accesso online:	https://ieeexplore.ieee.org/document/10735359/
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