  Tomographic SAR technique has attracted remarkable interest for its ability
of three-dimensional 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 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 paper proposes a novel 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 pre-computed 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. In addition,
adaptive threshold is introduced for each azimuth-range pixel, enabling the
threshold shrinkage to be not only layer-varied but also element-wise.
Moreover, 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.
