TY - JOUR
T1 - Solving Electromagnetic Inverse Scattering Problems in Inhomogeneous Media by Deep Convolutional Encoder-Decoder Structure
AU - Zhang, Huan Huan
AU - Yao, He Ming
AU - Jiang, Lijun
AU - Ng, Michael
N1 - This work was supported in part by the Hong Kong Research Grant Council General Research Fund (GRF) under Grant 12300218, Grant 12300519, Grant 17201020, Grant 17300021, Grant C1013-21GF, and Grant C7004-21GF; in part by the Joint Natural Science Foundation of China (NSFC)–Research Grants Council (RGC) under Grant N-HKU76921; and in part by the Fellowship Award from the Research Grants Council of the Hong Kong Special Administrative Region, China, under Grant HKU PDFS2122-7S05.
PY - 2023/3
Y1 - 2023/3
N2 - This communication proposes a novel deep learning (DL) approach to solve electromagnetic inverse scattering (EMIS) problems in inhomogeneous media. The conventional approaches for solving inhomogeneous EMIS problems generally have to consider inhomogeneous Green's functions or conduct approximation operations to media, which inevitably introduces various challenges, including complex mathematical derivation, high computation cost, unavoidable nonlinearity, and even strong ill-posedness. To avoid these challenges, we propose a DL approach based on the complex-valued deep convolutional neural networks (DConvNets), which comprise the deep convolutional encoder-decoder (DCED) structure. Its training data are collected based on the simple synthetic dataset. While the scattered fields received in the measurement domain are utilized as the input for the encoder to extract feature fragments, the final output for the counterpart decoder is the contrasts (permittivities) of dielectric scatterers in the target domain. In this way, unlike the conventional methods, the unknown domain between the target domain and measurement domain never has to be considered to compute inhomogeneous Green's functions. Consequently, the inhomogeneous EMIS problems could be solved with higher accuracy even for extremely high-contrast targets. Numerical examples illustrate the feasibility of the proposed DL approach. It acts as a new candidate for solving EMIS problems in inhomogeneous media.
AB - This communication proposes a novel deep learning (DL) approach to solve electromagnetic inverse scattering (EMIS) problems in inhomogeneous media. The conventional approaches for solving inhomogeneous EMIS problems generally have to consider inhomogeneous Green's functions or conduct approximation operations to media, which inevitably introduces various challenges, including complex mathematical derivation, high computation cost, unavoidable nonlinearity, and even strong ill-posedness. To avoid these challenges, we propose a DL approach based on the complex-valued deep convolutional neural networks (DConvNets), which comprise the deep convolutional encoder-decoder (DCED) structure. Its training data are collected based on the simple synthetic dataset. While the scattered fields received in the measurement domain are utilized as the input for the encoder to extract feature fragments, the final output for the counterpart decoder is the contrasts (permittivities) of dielectric scatterers in the target domain. In this way, unlike the conventional methods, the unknown domain between the target domain and measurement domain never has to be considered to compute inhomogeneous Green's functions. Consequently, the inhomogeneous EMIS problems could be solved with higher accuracy even for extremely high-contrast targets. Numerical examples illustrate the feasibility of the proposed DL approach. It acts as a new candidate for solving EMIS problems in inhomogeneous media.
KW - Deep learning (DL)
KW - electromagnetic inverse scattering (EMIS)
KW - high-contrast scatterer
KW - inhomogeneous media
UR - http://www.scopus.com/inward/record.url?scp=85148460152&partnerID=8YFLogxK
U2 - 10.1109/TAP.2023.3239185
DO - 10.1109/TAP.2023.3239185
M3 - Journal article
AN - SCOPUS:85148460152
SN - 0018-926X
VL - 71
SP - 2867
EP - 2872
JO - IEEE Transactions on Antennas and Propagation
JF - IEEE Transactions on Antennas and Propagation
IS - 3
ER -