dc.contributor.author | Aydın, İzde | en_US |
dc.contributor.author | Budak, Güven | en_US |
dc.contributor.author | Sefer, Ahmet | en_US |
dc.contributor.author | Yapar, Ali | en_US |
dc.date.accessioned | 2022-08-31T06:48:55Z | |
dc.date.available | 2022-08-31T06:48:55Z | |
dc.date.issued | 2022-08-18 | |
dc.identifier.citation | Aydın, İ., Budak, G., Sefer, A. & Yapar, A. (2022). Recovery of impenetrable rough surface profiles via CNN-based deep learning architecture. International Journal of Remote Sensing, 43(15-16) 5658-5685. doi:10.1080/01431161.2022.2105177 | en_US |
dc.identifier.issn | 0143-1161 | |
dc.identifier.issn | 1366-5901 | |
dc.identifier.uri | https://hdl.handle.net/11729/4802 | |
dc.identifier.uri | http://dx.doi.org/10.1080/01431161.2022.2105177 | |
dc.description.abstract | In this paper, a convolutional neural network (CNN)-based deep learning (DL) architecture for the solution of an electromagnetic inverse problem related to imaging of the shape of the perfectly electric conducting (PEC) rough surfaces is addressed. The rough surface is illuminated by a plane wave and scattered field data is obtained synthetically through the numerical solution of surface integral equations. An effective CNN-DL architecture is implemented through the modelling of the rough surface variation in terms of convenient spline type base functions. The algorithm is numerically tested with various scenarios including amplitude only data and shown that it is very effective and useful. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Taylor and Francis Ltd. | en_US |
dc.relation.isversionof | 10.1080/01431161.2022.2105177 | |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Convolutional neural network | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Inverse scattering problems | en_US |
dc.subject | Rough surface imaging | en_US |
dc.subject | Reconstruction | en_US |
dc.subject | Deep neural networks | en_US |
dc.subject | Electric conductance | en_US |
dc.subject | Integral equations | en_US |
dc.subject | Inverse problems | en_US |
dc.subject | Network architecture | en_US |
dc.subject | Surface measurement | en_US |
dc.subject | Surface scattering | en_US |
dc.subject | Electromagnetic inverse problems | en_US |
dc.subject | Learning architectures | en_US |
dc.subject | Network-based | en_US |
dc.subject | Rough surfaces | en_US |
dc.subject | Surface imaging | en_US |
dc.subject | Surface profiles | en_US |
dc.subject | Convolution | en_US |
dc.title | Recovery of impenetrable rough surface profiles via CNN-based deep learning architecture | en_US |
dc.type | article | en_US |
dc.description.version | Publisher's Version | en_US |
dc.relation.journal | International Journal of Remote Sensing | en_US |
dc.contributor.department | Işık Üniversitesi, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | en_US |
dc.contributor.department | Işık University, Faculty of Engineering, Department of Electrical-Electronics Engineering | en_US |
dc.contributor.authorID | 0000-0001-5168-4367 | |
dc.identifier.volume | 43 | |
dc.identifier.issue | 15-16 | |
dc.identifier.issue | SI | |
dc.identifier.startpage | 5658 | |
dc.identifier.endpage | 5685 | |
dc.peerreviewed | Yes | en_US |
dc.publicationstatus | Published | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.contributor.institutionauthor | Sefer, Ahmet | en_US |
dc.relation.index | WOS | en_US |
dc.relation.index | Scopus | en_US |
dc.relation.index | Science Citation Index Expanded (SCI-EXPANDED) | en_US |
dc.description.quality | Q2 | |
dc.description.wosid | WOS:000836577000001 | |