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Abstract：
In this paper, artificial intelligence (AI) technology is applied to the electromagnetic imaging of anisotropic objects. Advances in magnetic anomaly sensing systems and electromagnetic imaging use electromagnetic principles to detect and characterize subsurface or hidden objects. We use measured multifrequency scattered fields to calculate the initial dielectric constant distribution of anisotropic objects through the backpropagation scheme (BPS). Later, the estimated multifrequency permittivity distribution is input to a convolutional neural network (CNN) for the adaptive moment estimation (ADAM) method to reconstruct a more accurate image. In the meantime, we also improve the definition of loss function in the CNN. Numerical results show that the improved loss function unifying the structural similarity index measure (SSIM) and root mean square error (RMSE) can effectively enhance image quality. In our simulation environment, noise interference is considered for both TE (transverse electric) and TM (transverse magnetic) waves to reconstruct anisotropic scatterers. Lastly, we conclude that multifrequency reconstructions are more stable and precise than single-frequency reconstructions.
摘要：
在本文中,人工智能(AI)技术应用于各向异性物体的电磁成像。磁异常传感系统和电磁成像的进展使用电磁原理来检测和表征地下或隐藏物体。我们使用测量的多频散射场通过反向传播方案（BPS）计算各向异性物体的初始介电常数分布。稍后，将估计的多频介电常数分布输入到卷积神经网络（CNN），用于自适应矩估计（ADAM）方法，以重建更准确的图像。同时,我们还改进了CNN中损失函数的定义。数值结果表明，将结构相似指数度量（SSIM）和均方根误差（RMSE）统一的改进损失函数可以有效地提高图像质量。在我们的模拟环境中，TE（横向电）和TM（横向磁）波都考虑了噪声干扰，以重建各向异性散射体。最后,我们得出的结论是，多频重建比单频重建更稳定和精确。
