A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging

Deep learning (DL)-based tomographic SAR im-aging algorithms are gradually being studied. Typically, they use an unfolding network to mimic the iterative calculation of the classical compressive sensing (CS)-based methods and pro-cess each range-azimuth unit individually. However, only one-dimensional features are effectively utilized in this way. The cor-relation between adjacent resolution units is ignored directly. To address that, we propose a new model-data-driven network to achieve tomoSAR imaging based on multi-dimensional features. Guided by the deep unfolding methodology, a two-dimensional deep unfolding imaging network is constructed. On the basis of it, we add two 2D processing modules, both convolutional en-coder-decoder structures, to enhance multi-dimensional features of the imaging scene effectively. Meanwhile, to train the proposed multi-feature-based imaging network, we construct a tomoSAR simulation dataset consisting entirely of simulation data of buildings. Experiments verify the effectiveness of the model. Compared with the conventional CS-based FISTA method and DL-based γ-Net method, the result of our proposed method has better performance on completeness while having decent imaging accuracy.