Publication: CNN-Based Deep Learning Architecture for Electromagnetic Imaging of Rough Surface Profiles
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Institute of Electrical and Electronics Engineers (IEEE)
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Abstract
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
Description
Journal or Series
IEEE Transactions on Antennas and Propagation
ISSN
0018-926X
ISBN
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CLOSED
Keywords
Inverse problems, Neural-network, Inverse scattering problems, Network-based, Surface treatment, Inverse scattering, Method of moments, Convolutional neural network, Surface measurement, Imaging, Surface roughness, Surface scattering, Integral equations, Rough surface imaging, Network architecture, Surface imaging, Electromagnetics, Deep learning, Surface waves, Classification, Rough surfaces, Convolution, Numerical methods, Reconstruction, Neural networks