ATASI-Net: An Efficient Sparse Reconstruction Network for Tomographic SAR Imaging With Adaptive Threshold

Tomographic synthetic aperture radar (SAR) technique has attracted remarkable interest for its ability of 3-D resolving along the elevation direction via a stack of SAR images collected from different cross-track angles. The emerged compressed sensing (CS)-based algorithms have been introduced into SAR tomography (TomoSAR) considering its super-resolution ability with limited samples. However, the conventional CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity, and complex parameter fine-tuning. Aiming at efficient TomoSAR imaging, this article proposes a novel and efficient sparse unfolding network based on the analytic learned iterative shrinkage-thresholding algorithm (ALISTA) architecture with adaptive threshold, named adaptive threshold ALISTA-based sparse imaging network (ATASI-Net). The weight matrix in each layer of ATASI-Net is precalculated as the solution of an off-line optimization problem, leaving only two scalar parameters to be learned from data, which significantly simplifies the training stage. Furthermore, the introduction of an adaptive threshold for each azimuth–range pixel permits the threshold shrinkage to be not only layer-varied but also elementwise. In addition, the final learned thresholds can be visualized and combined with the SAR image semantics for mutual feedback. Finally, extensive experiments on simulated and real data are carried out to demonstrate the effectiveness and efficiency of the proposed method.