MSFormer: A Multisparsity Transformer for Reconstructing Missing Traces in GPR B-Scans via Self-Supervised Learning

The integrity of ground-penetrating radar (GPR) B-scans is essential for reliable interpretation of the object’s information. However, acquiring complete and high-quality B-scans is often hindered by field obstructions and measurement inefficiencies, leading to severely corrupted data. In thi...

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Autors principals:	Jiwei Qian, Yee Hui Lee, Kaixuan Cheng, Nicholas Lim, Jonathan Goh, Abdulkadir C. Yucel
Format:	Artigo
Idioma:	Inglês
Publicat:	IEEE 2026-01-01
Col·lecció:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Matèries:	Deep learning ground-penetrating radar (GPR) missing trace reconstruction multisparsity transformer (MSFormer) self-supervised learning
Accés en línia:	https://ieeexplore.ieee.org/document/11498570/
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