CNN-Based deep learning architecture for electromagnetic imaging of rough surface profiles

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Tarih

2022-10

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Yayıncı

IEEE

Erişim Hakkı

info:eu-repo/semantics/closedAccess

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Özet

A convolutional neural network (CNN) based deep learning (DL) technique for electromagnetic imaging of rough surfaces separating two dielectric media is presented. The direct scattering problem is formulated through the conventional integral equations and the synthetic scattered field data is produced by a fast numerical solution technique which is based on Method of Moments (MoM). Two different special CNN architectures are designed and implemented for the solution of the inverse rough surface imaging problem wherein both random and deterministic rough surface profiles can be imaged. It is shown by a comprehensive numerical analysis that the proposed deep-learning (DL) inversion scheme is very effective and robust.

Açıklama

Anahtar Kelimeler

Convolutional neural network, Deep learning, Electromagnetics, Electromagnetics, Imaging, Inverse problems, Inverse scattering problems, Rough surface imaging, Rough surfaces, Surface roughness, Surface treatment, Surface waves, Convolution, Integral equations, Method of moments, Network architecture, Neural networks, Numerical methods, Surface measurement, Surface scattering, Network-based, Surface imaging, Inverse scattering, Neural-network, Reconstruction, Classification, 2-D

Kaynak

IEEE Transactions on Antennas and Propagation

WoS Q Değeri

Q1
Q1

Scopus Q Değeri

Cilt

70

Sayı

10

Künye

Aydın, İ., Budak, G., Sefer, A. & Yapar, A. (2022). CNN-Based deep learning architecture for electromagnetic imaging of rough surface profiles. IEEE Transactions on Antennas and Propagation, 70(10), 9752-9763. doi:10.1109/TAP.2022.3177493