  Synthetic aperture radar (SAR) tomography (TomoSAR) has attracted remarkable
interest for its ability in achieving three-dimensional reconstruction along
the elevation direction from multiple observations. In recent years, compressed
sensing (CS) technique has been introduced into TomoSAR considering for its
super-resolution ability with limited samples. Whereas, the CS-based methods
suffer from several drawbacks, including weak noise resistance, high
computational complexity and complex parameter fine-tuning. Among the different
CS algorithms, iterative soft-thresholding algorithm (ISTA) is widely used as a
robust reconstruction approach, however, the parameters in the ISTA algorithm
are manually chosen, which usually requires a time-consuming fine-tuning
process to achieve the best performance. Aiming at efficient TomoSAR imaging, a
novel sparse unfolding network named analytic learned ISTA (ALISTA) is proposed
towards the TomoSAR imaging problem in this paper, and the key parameters of
ISTA are learned from training data via deep learning to avoid complex
parameter fine-tuning and significantly relieves the training burden. In
addition, experiments verify that it is feasible to use traditional CS
algorithms as training labels, which provides a tangible supervised training
method to achieve better 3D reconstruction performance even in the absence of
labeled data in real applications.
