Dual-input Electromagnetic Inverse Scattering Algorithm Based on Improved U-net

In this paper, we propose a dual-input inversion method based on deep learning to improve the accuracy of electromagnetic imaging using the back propagation algorithm (BP). An improved U-Net network is utilized to reconstruct the scatterers. Unlike other deep learning inversion methods, we input both the scatterer distribution data from BP imaging and the scattered field data received by the antennas into the neural network for training. This approach leads to a more accurate prediction of scatterer positions and characteristics. Compared to predicting the scatterers using only the scattered field as input, adding the BP imaging results at the input provides the neural network with more information, significantly reduces the learning difficulty, minimizes errors, and enhances the quality of imaging. To address potential gradient vanishing and spatial information loss during network training, we integrate attention mechanisms and residual modules into the basic U-Net network. The former helps the network extract important relevant information under different contrast conditions, while the latter focuses on solving the problems of gradient vanishing and explosion. Simulation experiments confirm that our dual-input inversion method significantly reduces the average error, outperforming traditional single-input reconstruction methods.