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

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 17; pp. 19668 - 19681
• Main Authors: Dai, Qiqi, Lee, Yee Hui, Sun, Hai-Han, Qian, Jiwei, Yusof, Mohamed Lokman Mohd, Lee, Daryl, Yucel, Abdulkadir C.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Clutter; Clutter removal; contrastive learning; Data augmentation; Deep learning; diffusion model; Diffusion models; Diffusion processes; generative adversarial network; Generative adversarial networks; Ground penetrating radar; ground-penetrating radar (GPR); Machine learning; Noise generation; Radar; Random noise; Sparse matrices; Supervised learning; Training; Training data; Unsupervised learning

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...