# Electromagnetic Imaging for Buried Conductors Using Deep Convolutional Neural Networks  
[PERSON]  
1 Department of Electrical and Computer Engineering, Tamkang University, New Taipei City 251301, Taiwan; [EMAIL_ADDRESS] (K.-X.Y); [EMAIL_ADDRESS] (P.-H.C.)1 Department of Computer Information and Network Engineering, Lunghwa University of Science and Technology, Taoyuan City 333326, Taiwan; air1808 eednetnetnet  
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[PERSON]  
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[PERSON]  
1 Department of Electrical and Computer Engineering, Tamkang University, New Taipei City 251301, Taiwan; [EMAIL_ADDRESS] (P.-H.C.)1 Department of Computer Information and Network Engineering, Lunghwa University of Science and Technology, Taoyuan City 333326, Taiwan; air1808 eednetnetnetnet  
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[PERSON]  
1 Department of Electrical and Computer Engineering, Tamkang University, New Taipei City 251301, Taiwan; [EMAIL_ADDRESS]. in actual practice, target shapes are often unknown, uncertain, or even composed of multiple sub-targets. In order to overcome this barrier, more sophisticated techniques had to be introduced; the object was located wither in free space or half-space. The methods for solving inverse problems in free space can further be categorized into conventional iterative algorithms and deep learning (DL) methods. In the iterative algorithms method, [PERSON] proposed discrete dipole approximation to solve the inverse scattering problem for conductors according to the equivalence principle. The equivalent surface currents comprised numerous discrete dipole moments. The simulation results indicated that better imaging results, a circularly polarized wave was superior [1]. In 2022, [PERSON] proposed an Iterative multiscaling approach, a subspace-based optimization method, and an inversion method for buried conductors' image reconstruction. Note that he had considered both synthetic and experimental data in assessing the reconstruction accuracy [2]. In recent years, deep learning neural networks have developed rapidly in various fields. In 2015, [PERSON] proposed U-Net, which consists of a contracting path to capture context and a symmetric expanding path to enable precise localization on biomedical image segmentation applications [3]. There have been many successful applications, including electromagnetic calculations. To overcome the shortcut in nonlinear electromagnetic inverse scattering problems for dielectric objects in free space, in 2019, [PERSON] proposed three training schemes, including direct inversion, backpropagation, and dominant current schemes, to calculate the initial image and then input it to U-Net to reconstruct the image [4]. In 2020, [PERSON] applied an image translation network, named the complex-valued pix2 pix, which included two parts: a generator and a discriminator. Results show that the complex-valued pix2 pix could learn the mapping from the initial contrast to the real contrast in microwave imaging models [5]. In 2021, end-to-end scalable cascaded convolutional neural networks were proposed to solve ISP. High-resolution images could be obtained directly from the scattered field with the guiding of the multiresolution labels in the cascaded blocks [6]. In 2022, [PERSON] proposed the dominant current scheme and backpropagation scheme to calculate the preliminary permittivity distribution and then combine with U-Net to reconstruct the uniaxial objects [7]. However, these methods need the preliminary image; the reconstruction result by U-Net for buried objects is worse since it is not able to obtain good preliminary images by processing only the upper scattered field--that is, rather than the full space. For buried objects under the frequency domain, most researchers applied conventional algorithms to solve the electromagnetic inverse scattering problems. In 2015, [PERSON] proposed to use asynchronous particle swarm optimization to reconstruct permittivity of the two-dimensional inhomogeneous dielectric cylinder [8].