SAR Parametric Super-Resolution Image Reconstruction Methods Based on ADMM and Deep Neural Network

Abstract

The compressed sensing (CS)-based synthetic aperture radar (SAR) imaging methods have emerged as the standard approach to obtain super-resolution (SR) SAR images and achieve extraordinary performances. However, they face three challenges. First, this kind of method is mainly based on the point scattering model and not suitable for characterizing the line-segment-scattering and surface-scattering features of distributed targets. Second, the hyperparameters in these methods are hard to tune to optimal values. Third, due to a large amount of calculation, these methods are difficult to apply in practice. In this article, to solve these problems, we introduce the line-segment-scatterers (LSSs) and rectangular-plate-scatterers (RPSs) in SAR echo model to develop the SAR hybrid echo model and propose two SAR parametric SR image reconstruction methods based on solving a CS problem, where three penalties are utilized to exploit the sparsity of the point scatterers, LSSs, and RPSs, respectively. At the core of the first method is a direct solver called multicomponent alternating direction method of multipliers (MC-ADMM) solver that solves the CS problem quickly and iteratively based on closed derivative expressions. In contrast, the second method maps the MC-ADMM solver into a deep unfolded neural network, i.e., the parametric SR imaging network (PSRI-Net), which is faster, and the parameters can be automatically set to the optimum. Since all the parameters of the MC-ADMM solver are learned discriminatively through end-to-end training in PSRI-Net. Extensive simulation and practical experiments are carried out to demonstrate the effectiveness of the proposed methods.