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GPRInvNet: Deep learning-based ground-penetrating radar data inversion for tunnel linings / Bin Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 10 (October 2021) . - pp 8305 - 8325 Titre : GPRInvNet: Deep learning-based ground-penetrating radar data inversion for tunnel linings Type de document : Article/Communication Auteurs : Bin Liu, Auteur ; Yuxiao Ren, Auteur ; Hanchi Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 8305 - 8325 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] apprentissage profond [Termes IGN] cible cachée [Termes IGN] extraction de traits caractéristiques [Termes IGN] géolocalisation par radar pénétrant GPR [Termes IGN] reconstruction d'image [Termes IGN] revêtement [Termes IGN] tunnel Résumé : (auteur) A DNN architecture referred to as GPRInvNet was proposed to tackle the challenges of mapping the ground-penetrating radar (GPR) B-Scan data to complex permittivity maps of subsurface structures. The GPRInvNet consisted of a trace-to-trace encoder and a decoder. It was specially designed to take into account the characteristics of GPR inversion when faced with complex GPR B-Scan data, as well as addressing the spatial alignment issues between time-series B-Scan data and spatial permittivity maps. It displayed the ability to fuse features from several adjacent traces on the B-Scan data to enhance each trace, and then further condense the features of each trace separately. As a result, the sensitive zones on the permittivity maps spatially aligned to the enhanced trace could be reconstructed accurately. The GPRInvNet has been utilized to reconstruct the permittivity map of tunnel linings. A diverse range of dielectric models of tunnel linings containing complex defects has been reconstructed using GPRInvNet. The results have demonstrated that the GPRInvNet is capable of effectively reconstructing complex tunnel lining defects with clear boundaries. Comparative results with existing baseline methods also demonstrated the superiority of the GPRInvNet. For the purpose of generalizing the GPRInvNet to real GPR data, some background noise patches recorded from practical model testing were integrated into the synthetic GPR data to retrain the GPRInvNet. The model testing has been conducted for validation, and experimental results revealed that the GPRInvNet had also achieved satisfactory results with regard to the real data. Numéro de notice : A2021-710 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3046454 Date de publication en ligne : 13/01/2021 En ligne : https://doi.org/10.1109/TGRS.2020.3046454 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98610 [article]