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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/123313

    Title: Electromagnetic Imaging of Uniaxial Objects by Artificial Intelligence Technology
    Authors: Chien-Ching Chiu;Po-Hsiang Chen;Hao Jiang
    Keywords: Image reconstruction;Neural networks;Imaging;Deep learning;Permittivity;Inverse problems;Electromagnetics
    Date: 2022-12-16
    Issue Date: 2023-04-28 17:36:42 (UTC+8)
    Publisher: IEEE
    Abstract: The electromagnetic (EM) imaging of uniaxial objects by the artificial intelligence (AI) technology is presented in this article. We study the 2-D inverse scattering problem from uniaxial objects illuminated by the transverse magnetic (TM) and transverse electric (TE) polarized incident waves. As the uniaxial objects have different components of permittivity along different transverse directions, the problem of TE polarization will be more severe than that of TM polarization. We use the dominant current scheme (DCS) and backpropagation scheme (BPS) to calculate the preliminary permittivity distribution. By combining with deep learning and neural networks, the permittivity distribution of those uniaxial objects can be reconstructed more accurately. U-Net is used to reconstruct the permittivity distribution because U-Net has shared the weights and biases, which can effectively reduce the network complexity and is very suitable for solving image processing problems. In the numerical results, we added different noises to compare the reconstruction results of the DCS and BPS initial estimations through the U-Net. Numerical results show that the reconstruction permittivity for the DCS initial estimation is better than that for the BPS initial estimation. Our diversity is that we have reconstructed the uniaxial objects by neural network successfully with less time-consuming effort and real-time imaging.
    Relation: IEEE Transactions on Geoscience and Remote Sensing 60, 2008414
    DOI: 10.1109/TGRS.2022.3222502
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Journal Article

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