Recovery of impenetrable rough surface profiles via CNN-based deep learning architecture

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dc.authorid0000-0002-8428-4404
dc.authorid0000-0001-5168-4367
dc.authorid0000-0003-2966-5623
dc.contributor.authorAydın, İzdeen_US
dc.contributor.authorBudak, Güvenen_US
dc.contributor.authorSefer, Ahmeten_US
dc.contributor.authorYapar, Alien_US
dc.date.accessioned2022-08-31T06:48:55Z
dc.date.available2022-08-31T06:48:55Z
dc.date.issued2022-08-18
dc.departmentIşık Üniversitesi, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering, Department of Electrical-Electronics Engineeringen_US
dc.description.abstractIn 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.description.versionPublisher's Versionen_US
dc.identifier.citationAydın, 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:10.1080/01431161.2022.2105177en_US
dc.identifier.doi10.1080/01431161.2022.2105177
dc.identifier.endpage5685
dc.identifier.issn0143-1161
dc.identifier.issn1366-5901
dc.identifier.issue15-16
dc.identifier.issueSI
dc.identifier.scopus2-s2.0-85135483505
dc.identifier.scopusqualityQ1
dc.identifier.startpage5658
dc.identifier.urihttps://hdl.handle.net/11729/4802
dc.identifier.urihttp://dx.doi.org/10.1080/01431161.2022.2105177
dc.identifier.volume43
dc.identifier.wosWOS:000836577000001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakScience Citation Index Expanded (SCI-EXPANDED)en_US
dc.institutionauthorSefer, Ahmeten_US
dc.institutionauthorid0000-0001-5168-4367
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherTaylor and Francis Ltd.en_US
dc.relation.ispartofInternational Journal of Remote Sensingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConvolutional neural networken_US
dc.subjectDeep learningen_US
dc.subjectInverse scattering problemsen_US
dc.subjectRough surface imagingen_US
dc.subjectReconstructionen_US
dc.subjectDeep neural networksen_US
dc.subjectElectric conductanceen_US
dc.subjectIntegral equationsen_US
dc.subjectInverse problemsen_US
dc.subjectNetwork architectureen_US
dc.subjectSurface measurementen_US
dc.subjectSurface scatteringen_US
dc.subjectElectromagnetic inverse problemsen_US
dc.subjectLearning architecturesen_US
dc.subjectNetwork-baseden_US
dc.subjectRough surfacesen_US
dc.subjectSurface imagingen_US
dc.subjectSurface profilesen_US
dc.subjectConvolutionen_US
dc.titleRecovery of impenetrable rough surface profiles via CNN-based deep learning architectureen_US
dc.typeArticleen_US

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