Convolutional Neural Network-Based Electromagnetic Imaging of Uniaxial Objects in a Half-Space

In this paper, we adopt artificial intelligence (AI) technology for the electromagnetic imaging of uniaxial objects buried in a half-space environment. The limited measurement angle inherent to half-space configurations significantly increases the difficulty of data collection. This paper discusses the simultaneous emission of Transverse Magnetic (TM) and Transverse Electric (TE) electromagnetic waves to illuminate a uniaxial object embedded in a half-space. The dominant current scheme (DCS) and the backpropagation scheme (BPS) are subsequently employed to compute the initial permittivity distribution, which is then used as a dataset for training Convolutional Neural Networks (CNNs). The numerical results compare the reconstruction capabilities of both methods under identical conditions, demonstrating that the DCS exhibits superior generalization and noise immunity compared to the BPS. These findings confirm the effectiveness of both schemes in reconstructing the dielectric constant distribution of uniaxial objects buried in a half-space.