Microwave imaging of a periodic homogeneous dielectric object buried in rough surfaces

Chiu, Chien‐Ching; Lee, Gang-Ze; Jiang, Hao; Hong, Bo-Jie

doi:10.1080/09205071.2019.1653229

Cited by 11 publications

(4 citation statements)

References 23 publications

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“…In 2012, Pastorino introduced the Newton algorithm for reconstructing buried objects by employing numerical simulations [19]. In 2019, Chiu employed Self-Adaptive Dynamic Differential Evolution (SADDE) to regenerate buried dielectric objects under non-flat rough surfaces [20]. Continuing this trend, Huang presented a Full-Wave Inversion (FWI) method for buried anisotropic objects in 2021.…”

Section: Introductionmentioning

confidence: 99%

Application of Self-Attention Generative Adversarial Network for Electromagnetic Imaging in Half-Space

Chiu,

Lee,

Chen

et al. 2024

Sensors

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In this paper, we introduce a novel artificial intelligence technique with an attention mechanism for half-space electromagnetic imaging. A dielectric object in half-space is illuminated by TM (transverse magnetic) waves. Since measurements can only be made in the upper space, the measurement angle will be limited. As a result, we apply a back-propagation scheme (BPS) to generate an initial guessed image from the measured scattered fields for scatterer buried in the lower half-space. This process can effectively reduce the high nonlinearity of the inverse scattering problem. We further input the guessed images into the generative adversarial network (GAN) and the self-attention generative adversarial network (SAGAN), respectively, to compare the reconstruction performance. Numerical results prove that both SAGAN and GAN can reconstruct dielectric objects and the MNIST dataset under same measurement conditions. Our analysis also reveals that SAGAN is able to reconstruct electromagnetic images more accurately and efficiently than GAN.

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Section: Introductionmentioning

confidence: 99%

Application of Self-Attention Generative Adversarial Network for Electromagnetic Imaging in Half-Space

Chiu,

Lee,

Chen

et al. 2024

Sensors

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“…The microwave imaging technique still has urgent research issues such as poor penetration results when the signal-to-noise ratio is low, illposedness, nonlinearity, and large computational burden, as well as the parameter selection problem. To conquer the above problems, the inverse scattering problem is generally converted to an optimization problem, which can be solved by the 1) objective function method (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17) or 2) neural network learning method. (8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22) The objective function is generally used to find the extreme value of the problem, e.g., to calculate the minimum error of the scattering field and the measured scattering field.…”

Section: Introductionmentioning

confidence: 99%

“…The gradient method is more suitable for small-area searches, (4) whereas the global algorithm is suitable for full-area searches. (2) The whale optimization algorithm was applied to the inverse scattering of an imperfect conductor with corners in 2020. (6) The effect of incident field polarization was analyzed for inverse metal object scattering by discrete dipole approximation.…”

Section: Introductionmentioning

confidence: 99%

Microwave Imaging of Conductors by Direct Sampling Method and U-Net

Chiu¹,

Li²,

Chien³

et al. 2023

Sensors and Materials

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Electromagnetic imaging is an emerging technology widely applied in many fields, such as medical imaging, biomedical imaging, and nondestructive testing. In this study, we place transmitter and receiver antennas around an unknown object. We can use the direct sampling method (DSM) to reconstruct the material size and shape of the unknown object on the basis of the scattered field. We apply U-Net to reconstruct electromagnetic images of perfect conductors. Perfect conductors in free space are studied by irradiating a transverse magnetic (TM) polarization wave. Using the scattered electric field measured outside the object together with the boundary conditions on the conductor surface, a set of nonlinear integral equations can be derived and further converted into matrix form by the method of moments. Since an iterative algorithm is computationally expensive and time-consuming, a real-time electromagnetic imaging technique combining deep learning neural networks is proposed for reconstructing the perfect conductors. The initial shapes of the conductors are first computed by DSM by using the scattered electric field measured outside the object. The initial shapes of the conductors are then input to U-Net for training. Numerical results show that U-Net is capable of reconstructing accurate conductor shapes. Therefore, artificial intelligence techniques can reconstruct shapes more accurately than iterative algorithms, when combined with DSM.

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“…In 2015, Lee proposed to use asynchronous particle swarm optimization to reconstruct permittivity of the twodimensional inhomogeneous dielectric cylinder [8]. In 2019, Chiu proposed to use a selfadaptive dynamic differential evolution method to reconstruct the periodic homogeneous dielectric object buried in rough surfaces [9]. However, these methods will spend a lot of time in iterative calculation of the complex Green's function.…”

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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Microwave imaging of a periodic homogeneous dielectric object buried in rough surfaces

Cited by 11 publications

References 23 publications

Application of Self-Attention Generative Adversarial Network for Electromagnetic Imaging in Half-Space

Application of Self-Attention Generative Adversarial Network for Electromagnetic Imaging in Half-Space

Microwave Imaging of Conductors by Direct Sampling Method and U-Net

Electromagnetic Imaging for Buried Conductors Using Deep Convolutional Neural Networks

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