Enhanced SAR Imagery by Autoblocking Approach Using Morphology Amoeba in ADMM Manner

Abstract

For conventional structure-guaranteed synthetic aperture radar (SAR) imagery problems, some of them are modeled as 1 / 2 mixed norm regularization, and some are modeled as a group of least absolute shrinkage and selection operator. However, due to the inflexible prior based on the Euclidean distance blocking, the target of interests and the background noise are easily attributed into the same block, which will consequently be retained or removed simultaneously. Therefore, it is difficult to distinguish the target with rich structures in a complicated environment. In this article, to realize the elaborated structure feature enhancement of interested targets in the echoed SAR data, a novel morphological autoblocking-alternating direction method of multipliers (MAB-ADMM) SAR imaging algorithm is proposed with a hybrid Frobenius and morphology F / M norm regularization. To realize the flexible blocking, an autoblocking mechanism is constructed by spatially-variant morphological amoeba (SVMA) in the F / M mixed norm. In the autoblocking mechanism, the geodesic distance of SVMA is utilized to accurately and flexibly fit the target structure and guarantee the elaborated structure. Moreover, in the proposed MAB-ADMM algorithm, the flexibly blocking structure feature and intrablock sparse feature are jointly optimized by the F / M and 1 norm. In the experiments, both simulated and raw SAR data collected by an airborne SAR are applied to qualitatively evaluate the proposed algorithm. Comparisons with conventional methods are carried out to reveal the superiority of the proposed algorithm. Moreover, the phase transition diagram is shown to verify the proposed algorithm in terms of quantitative aspects.