CNN-Based deep learning architecture for electromagnetic imaging of rough surface profiles
| Autor: | Izde Aydin, Guven Budak, Ahmet Sefer, Ali Yapar |
|---|---|
| Přispěvatelé: | Işık Üniversitesi, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü, Işık University, Faculty of Engineering, Department of Electrical-Electronics Engineering, Sefer, Ahmet |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Inverse problems
Neural-network Inverse scattering problems Network-based Surface treatment Inverse scattering Method of moments Convolutional neural network Surface measurement Imaging Surface roughness Electrical and Electronic Engineering Surface scattering Integral equations Rough surface imaging 2-D Network architecture Surface imaging Electromagnetics Deep learning Surface waves Classification Rough surfaces Convolution Numerical methods Reconstruction Neural networks |
| Popis: | 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. Publisher's Version Q1 WOS:000880709700101 |
| Databáze: | OpenAIRE |
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