PUP-Net: A Twofold Physical Model Embedded 3-D U-Net With Polarization Fusion for Solving Inverse Scattering Problems With a Sparse Planar Array

Abstract

In this article, a twofold physical model (PM) embedded 3-D U-Net with polarization fusion (PF), referred to as PUP-Net, is proposed for solving 3-D inverse scattering problems (ISPs) with a sparse planar array. To mitigate the high nonlinearity of ISPs, the twofold PM, consisting of the coherence factor back-projection algorithm (CF-BPA) and cross-correlated subspace-based optimization method (CC-SOM), first recovers the preliminary quantitative co-polarization images with high-fidelity geometry. The preliminary results are then input into the 3-D U-Net-based convolutional neural network (CNN), which outputs the fine contrast images of co-polarization (HH and VV). Finally, the upsampling PF achieves the co-polarization image fusion and generates higher accuracy and resolution results. PUP-Net follows the divide-and-conquer method: refine quantitative inversion based on good qualitative inversion. Synthetic and experimental inversion results demonstrate that PUP-Net achieves superior reconstruction accuracy and generalization ability compared with conventional iterative methods and current deep learning (DL) approaches in solving ISPs of half-space configurations.