Video SAR Imaging Based on Low-Rank Tensor Recovery

Due to its ability of forming continuous images for a ground scene of interest, the video synthetic aperture radar (SAR) has been studied in recent years. However, as video SAR needs to reconstruct many frames, the data are of enormous amount and the imaging process is of large computational cost, which limits its applications. In this article, we exploit the redundancy property of multiframe video SAR data, which can be modeled as low-rank tensor, and formulate the video SAR imaging process as a low-rank tensor recovery problem, which is solved by an efficient alternating minimization method. We empirically compare the proposed method with several state-of-the-art video SAR imaging algorithms, including the fast back-projection (FBP) method and the compressed sensing (CS)-based method. Experiments on both simulated and real data show that the proposed low-rank tensor-based method requires significantly less amount of data samples while achieving similar or better imaging performance.

Automated summary

In this article, the authors study the low-rank tensor recovery problem of video synthetic aperture radar (SAR) imaging, which reduces the data amount by exploiting the redundancy property of multi-frame video SAR data, achieving similar or better imaging performance. The proposed method compares favorably with several state-of-the-art video SAR imaging algorithms in terms of data sample requirements and imaging quality.