A 3-D Imaging Method Of Building With Tomosar Based On DUADMM-Net

Tomographic SAR (TomoSAR) can achieve 3-D imaging for observation targets through tomographic synthetic aperture, and shows good characteristics in urban building information extraction and scene 3-D inversion. Though the existing CS-based imaging algorithms can achieve high-resolution imaging results, requiring multiple iterations and manual adjustment of hyper-parameters. Currently, deep learning techniques in TomoSAR show great advantages in improving the imaging accuracy and efficiency. Inspired by deep unfolding, we unfolded the CS-based ADMM algorithm and mapped it into deep unfolded ADMM-net (DUADMM-net), so as to achieve high-resolution TomoSAR imaging. DUADMM-net consists of reconstructed signal estimation module, nonlinear fitting module and multiplier update module. The introduction of convolutional layers enhances the learning ability and nonlinear fitting ability. Compared to the conventional sparse imaging algorithms, the experimental imaging results and quantitative indicators demonstrate the effectiveness and efficiency of DUADMM-net.