Super-Resolution Tomographic SAR Imaging with Deep LISTA Network

Synthetic aperture radar (SAR) tomography (TomoSAR) can separate the layover scatterers and obtain the position and reflectivity of each scatterer to realize the 3-D reconstruction. The compressive-sensing based algorithms have been widely used in TomoSAR inversion. However, this superior performance comes at large computational cost. In this article, we propose a deep learning based TomoSAR inversion approach, which is the learned iterative shrinkage thresholding algorithm (LISTA). Since the network parameters can be trained, the LISTA network can obtain more accurate reconstruction. The pretrained network can also greatly improve the computational efficiency. The simulation experiments show that the LISTA algorithm has super-resolution performance and can effectively separate the layover scatterers.