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Vol. 34, No. 8(3), S&M3042

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Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
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Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials, Volume 36, Number 7(2) (2024)
Copyright(C) MYU K.K.
pp. 2925-2941
S&M3710 Research Paper of Special Issue
https://doi.org/10.18494/SAM5018
Published: July 24, 2024

Generative Adversarial Network Applied to Electromagnetic Imaging of Buried Objects [PDF]

Chien-Ching Chiu, Wei Chien, Ching-Lieh Li, Po-Hsiang Chen, Kai-Xu Yu, and Eng-Hock Lim

(Received February 2, 2024; Accepted June 20, 2024)

Keywords: buried dielectric object, electromagnetic imaging, inverse scattering problems, generative adversarial network

Generative adversarial network (GAN) architecture is employed to tackle the inverse scattering problem of buried dielectric objects in half-space. Traditional iterative methods aimed at resolving the inverse scattering problem of buried dielectric objects have encountered a variety of difficulties, such as highly nonlinear phenomenon, high computational costs for half-space Green’s function, and missing measured scattered field information at the lower half of the object. The generator of GAN learns to generate more realistic images, while the discriminator of GAN improves its ability to identify fake images through a game-like process. The iterative process stops when the image generated by the generator is indistinguishable from the real image. In addition, we also analyze and compare the reconstruction outcomes obtained using both GAN and U-Net. Numerical outcomes show that GAN can efficiently reconstruct images with higher reliability than U-Net for buried objects with different dielectric permittivities and handwritten shapes. In summary, our proposed method has opened up a new avenue for imaging buried objects by adopting a deep learning network technique.

Corresponding author: Chien-Ching Chiu


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This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Chien-Ching Chiu, Wei Chien, Ching-Lieh Li, Po-Hsiang Chen, Kai-Xu Yu, and Eng-Hock Lim, Generative Adversarial Network Applied to Electromagnetic Imaging of Buried Objects, Sens. Mater., Vol. 36, No. 7, 2024, p. 2925-2941.



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