SAR Imaging of Moving Targets by Subaperture based Low-rank and Sparse Decomposition
MSc. Thesis, 2017
Assoc. Prof. Dr. Müjdat Çetin (Thesis Advisor), Assist. Prof. Dr. Kamer Kaya, and Assist. Prof. Dr. N. Özben Önhon (external jury member)
Date & Time: 1st August, 2017 – 03:00 PM
Keywords : Synthetic aperture radar (SAR), SAR imaging, moving targets, low-rank and sparse decomposition, subaperture processing
Synthetic aperture radar (SAR) has gained significance as an indispensable instrument of remote sensing and airborne surveillance. Its applications extend to 3D terrain mapping, oil spill detection, crop yield estimation and disaster evaluation. SAR utilizes platform motion to synthesize a large antenna thus rendering a very fine spatial resolution. Nevertheless, imaging of moving targets with SAR is a challenging problem. In this thesis, we propose a moving target imaging approach for SAR which exploits the low-rank and sparse decomposition (LRSD) of the subaperture data. As a first step, multiple subapertures are constructed from the raw data using frequency domain filtering. In contrast to the stationary points, moving targets in the SAR scene shift their position in the various subapertures. This enables a successful low-rank and sparse decomposition of the subaperture data where the sparse component captures the moving targets' phase histories and reflectivity profiles. On the other hand, the low-rank component consists of the static background due to fewer spatial variations in multiple subapertures. This framework allows the reconstruction of full-resolution sparse and low-rank images by combining the spectral information of the decomposed subapertures. Furthermore, it enhances the applicability of sparsity-driven moving target imaging frameworks to very low signal to clutter ratio (SCR) scenarios by offering a considerable SCR performance improvement. We manifest the effectiveness of our approach through experiments with synthetic as well as real SAR data. Our real SAR experiments were based on MiniSAR and EMISAR data.