Enhanced Deep Learning Approach Based on the Deep Convolutional Encoder-Decoder Architecture for Electromagnetic Inverse Scattering Problems

This letter proposes a novel deep learning (DL) approach to resolve the electromagnetic inverse scattering (EMIS) problems. The conventional approaches of resolving EMIS problems encounter assorted difficulties, such as high contrast, high computational cost, inevitable intrinsic nonlinearity, and s...

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Bibliographic Details
Published in:IEEE antennas and wireless propagation letters Vol. 19; no. 7; pp. 1211 - 1215
Main Authors: Yao, He Ming, Jiang, Lijun, Sha, Wei E. I.
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Artificial neural networks
Coders
Convolutional neural network
Decoding
Deep learning
deep learning (DL)
Electromagnetic interference
electromagnetic inverse scattering (EMIS)
Encoding
Feature extraction
Fragments
high-contrast scatterer
Inverse scattering
Machine learning
Mapping
Mathematical model
Receivers
Scattering
ISSN:1536-1225, 1548-5757
Online Access:Get full text
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Summary:This letter proposes a novel deep learning (DL) approach to resolve the electromagnetic inverse scattering (EMIS) problems. The conventional approaches of resolving EMIS problems encounter assorted difficulties, such as high contrast, high computational cost, inevitable intrinsic nonlinearity, and strong ill-posedness. To surmount these difficulties, a novel DL approach is proposed based on a novel complex-valued deep fully convolutional neural network structure. The proposed complex-valued deep learning model for solving EMIS problems composes of an encoder network and its corresponding decoder network, followed by a final pixel-wise regression layer. The complex-valued encoder network extracts feature fragments from received scattered field data, while the role of the complex-valued decoder network is mapping the feature fragments to retrieve the final contrasts (permittivities) of dielectric scatterers. Hence, the proposed deep learning model functions as an "heterogeneous" transformation process, where measured scattering field data is converted into the corresponding contrasts of scatterers. As a consequence, the EMIS problem could be resolved accurately even for extremely high-contrast targets. Numerical benchmarks have illustrated the feasibility and accuracy of our proposed approach. The proposed approach opens a novel path for the deep learning-based real-time quantitative microwave imaging for high-contrast scatterers.
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ISSN:1536-1225
1548-5757
DOI:10.1109/LAWP.2020.2995455