Accurate and robust inversion of buried objects based on the linear sampling method and differential evolution

Fan, Qimeng; Yin, Chengyou; Gan, Yongji

doi:10.1049/iet-rsn.2019.0422

Cited by 1 publication

(1 citation statement)

References 37 publications

(40 reference statements)

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“…A relaxed error threshold was defined for LSM to ensure all real targets had been included. Second, differential evolution optimization was used to refine the LSM results [10]. In 2022, Ren proposed a method for reconstructing media objects buried in three layers of media by using 2D projection as well as the shadow projections of 3D images.…”

Section: Introductionmentioning

confidence: 99%

Electromagnetic Imaging for Buried Conductors Using Deep Convolutional Neural Networks

Chiu

Chien

et al. 2023

Applied Sciences

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In the past, many conventional algorithms, such as self-adaptive dynamic differential evolution and asynchronous particle swarm optimization, were used to reconstruct buried objects in the frequency domain; these were unfortunately time-consuming during the iterative, repeated computing process of the scattered field. Consequently, we propose an innovative deep convolutional neural network approach to solve the electromagnetic inverse scattering problem for buried conductors in this paper. Different shapes of conductors are buried in one half-space and the electromagnetic wave from the other half-space is incident. The shape of the conductor can be reconstructed promptly by inputting the received scattered fields measured from the upper half-space into the deep convolutional neural network module, which avoids the computational complexity of Green’s function for training. Numerical results show that the root mean square error for differently shaped—circular, elliptical, arrow, peanut, four-petal, and three-petal—reconstructed images are, respectively, 2.95%, 3.11%, 17.81%, 15.10%, 14.14%, and 15.24%. Briefly speaking, not only can circular and elliptical buried conductors be reconstructed; some irregular shapes can be reconstructed well. On the contrary, the reconstruction result by U-Net for buried objects is worse since it is not able to obtain a good preliminary image by processing only the upper scattered field—that is, rather than the full space. In other words, our proposed deep convolutional neural network can efficiently solve the electromagnetic inverse scattering problem of buried conductors and provide a novel method for the microwave imaging of the buried conductors. This is the first successful attempt at using deep convolutional neural networks for buried conductors in the frequency domain, which may be useful for practical applications in various fields such as the medical, military, or industrial fields, including magnetic resonance imaging, mine detection and clearance, non-destructive testing, gas or wire pipeline detection, etc.

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Section: Introductionmentioning

confidence: 99%