Statistical regularization in synthetic aperture imaging radiometry

Synthetic aperture imaging radiometers (SAIRs) are powerful instruments for high-resolution observation of planetary surface at microwave band. In order to reconstruct the brightness temperature maps from the inteferometric measurements stably and uniquely, it has been recommended to cure the corresponding ill-posed problem with the aid of regularization framework. However, the performances of such numerical regularized solutions highly depend on manually choosing the regularized parameters. In this study, we proposed a statistical regularization method to estimate the optimal regularized parameter of the SAIR inversion adaptively. Furthermore, we have carried out some numerical simulations in reference to the SAIR inversion, and relative comparative analysis has been accomplished to validate the proposed method.