L-Hypersurface Based Parameters Selection in Composite Regularization Models With Application to SAR and TomoSAR Imaging

Abstract

Composite regularization models are widely used in sparse signal processing, making multiple regularization parameters selection a significant problem to be solved. Variety kinds of composite regularization models are used in sparse microwave imaging, including $\ell _{1}$ and $\ell _{2}$ penalty, nonconvex and total variation penalty, combined dictionary, etc. In this article, a new adaptive multiple regularization parameters selection method named L-hypersurface is proposed. The effectiveness of the proposed method is verified by experiments. Simulation experiments indicate that the selected optimal regularization parameters have satisfied reconstruction results, both visually and numerically. Furthermore, experiments on Gaofen-3 synthetic aperture radar satellite data are also exploited to show the performance of the proposed method.

Keywords

• Composite regularization
• l-hypersurface
• regularization parameter selection
• sparse signal processing
• synthetic aperture radar (SAR)
• tomographic SAR (TomoSAR)