  Tomographic synthetic aperture radar (TomoSAR) imaging algorithms based on
deep learning can effectively reduce computational costs. The idea of existing
researches is to reconstruct the elevation for each range-azimuth cell in
one-dimensional using a deep-unfolding network. However, since these methods
are commonly sensitive to signal sparsity level, it usually leads to some
drawbacks like continuous surface fractures, too many outliers, \textit{et al}.
To address them, in this paper, a novel imaging network (AETomo-Net) based on
multi-dimensional features is proposed. By adding a U-Net-like structure,
AETomo-Net performs reconstruction by each azimuth-elevation slice and adds 2D
features extraction and fusion capabilities to the original deep unrolling
network. In this way, each azimuth-elevation slice can be reconstructed with
richer features and the quality of the imaging results will be improved.
Experiments show that the proposed method can effectively solve the above
defects while ensuring imaging accuracy and computation speed compared with the
traditional ISTA-based method and CV-LISTA.
