Robust Polarimetric Sar Imaging Method With Attributed Scattering Characterization

Conventional ideal point model-based synthetic aperture radar (SAR) imaging techniques may lead to scattering estimation inaccurate and object-level information loss because they ignore the frequency, angle, and polarimetric dependence of canonical scatterers. These issues are especially severe when SAR measurement is contaminated with impulsive noise. To tackle the problem, in this paper, we propose a robust polarimetric SAR imaging method with attributed scattering priors in the framework of compressive sensing. In this scheme, the overcomplete dictionary is formed by taking advantage of the physically relevant attributed scattering center (ASC) model, which is capable to represent the electromagnetic behaviors of man-made assemblies. By using the robust data-fitting model and employing the joint sparsity of multiple polarization measurements, the problem of estimating ASC attributes can be converted into robust group sparse recovery via l(1)-l(2,1) optimization. To solve this formulation efficiently, we develop an alternating direction method of multipliers framework via exploiting the proximity operator of complex l(1) norm and l(2) norm functions for complex-valued SAR images. Finally, the super-resolution SAR image is achieved via spectrum extrapolation based on the estimated ASC attributes. The proposed technique can enhance the SAR image features arising from the non-ideal scattering components of the target and gain robust performance to impulsive noise. The experimental results confirm the effectiveness and efficiency of the proposed algorithm.