3-D SAR Data-Driven Imaging via Learned Low-Rank and Sparse Priors

In the research topic of three-dimensional (3-D) synthetic aperture radar (SAR) imaging, the sparsity-enforcing techniques offer promise in shortening the sensing time and improving the reconstruction accuracy. However, many of them only explore the sparse prior of 3-D SAR images, which leads to biased estimations in cases of non-sparse scenarios. To remedy this problem, we propose a new network with learned low-rank and sparse priors, i.e., LLRS-Net, to obtain improved reconstructions from sparsely sampled 3-D SAR echoes. In our scheme, a two-stage reconstruction algorithmic framework (LSRA) is derived based on sparse and low-rank priors, wherein the first stage recovers the measurements from their limited observations by exploring the low-rank prior, while the second estimates the final 3-D SAR images with a fast iterative optimization. Theoretically inspired by LRSA, the LLRS-Net is designed into a cascaded network structure. In LLRS-Net, the trainable weights serve as independent variables and control the algorithmic hyperparameters via regularizing functions, ensuring a well-conditioned updating tendency. By end-to-end training, the network weights are updated automatically under the guidance of a compound loss function constraining both the outputs of two stages. Finally, the methodology is validated on simulations and measured experiments. These results show that the proposed framework outperforms many state-of-the-art imaging algorithms in recovering 3-D SAR images from incomplete echo data.