On Solving SAR Imaging Inverse Problems Using Non-Convex Regularization with a Cauchy-based Penalty

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

Synthetic aperture radar (SAR) imagery can provide useful information in a multitude of applications, including climate change, environmental monitoring, meteorology, high dimensional mapping, ship monitoring, or planetary exploration. In this article, we investigate solutions for several inverse problems encountered in SAR imaging. We propose a convex proximal splitting method for the optimization of a cost function that includes a nonconvex Cauchy-based penalty. The convergence of the overall cost function optimization is ensured through careful selection of model parameters within a forward-backward (FB) algorithm. The performance of the proposed penalty function is evaluated by solving three standard SAR imaging inverse problems, including super-resolution, image formation, and despeckling, as well as ship wake detection for maritime applications. The proposed method is compared to several methods employing classical penalty functions such as total variation (TV) and L₁ norms, and to the generalized minimax-concave (GMC) penalty. We show that the proposed Cauchy-based penalty function leads to better image reconstruction results when compared to the reference penalty functions for all SAR imaging inverse problems in this article.
Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalIEEE Transactions on Geoscience and Remote Sensing
DOIs
Publication statusE-pub ahead of print - 6 Aug 2020

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