Structure-Guaranteed SAR Imagery via Spatially-Variant Morphology Regularization in ADMM Manner

Abstract

Conventional sparsity-driven synthetic aperture radar (SAR) imagery proceeds via ℓ₁ regularization, or named by least absolute shrinkage and selection operator (LASSO). However, followed by the enhanced sparse feature, structures of the scenes or targets of interests in weak scattering are easily lost. Therefore, it becomes difficult to make use of the high-resolution SAR data, even high costs have been paid for the resolution. In this paper, a novel structure-guaranteed SAR (SG-SAR) imaging algorithm is proposed by utilizing the morphology metric for the cluster feature of the scatterers of the scenes/targets of interests. By introducing structural prior in terms of morphology norm, the intended structure features can be highlighted via convex regularization. More specifically, to accommodate to complicated scenarios or targets, the structure element in the morphology regularizer is designed to be spatially variant under structure tensor representation. Different from conventional convex optimizations, the proposed SG-SAR algorithm is solved under alternating direction method of multipliers (ADMM), which is flexible to incorporate with the super-resolution imagery. In such cases, both sparse and structural features can be simultaneously enhanced, even with limited measurements and in low signal-to-noise ratio (SNR). Superior convergence and robustness can be guaranteed. Moreover, a grouping mask scheme is used to accommodate to the complex-valued SAR data. Finally, both simulated and measured SAR data are applied for the validation. Comparisons with the conventions are performed in terms of phase transition analysis, so as to verify the superiority of the proposed algorithm both qualitatively and quantitatively.