Auto-Learning of Parameters for High Resolution Sparse Group Lasso SAR Imagery

Abstract

Aiming at the problem of adjusting the penalty term coefficient of feature enhancement in high-resolution synthetic aperture radar (SAR) imaging, a marginal estimation Bayes (MEB) algorithm is proposed, so that the prior features of the target can be fitted properly to improve the accuracy of image feature extraction. Firstly, the alternating direction method of multipliers (ADMM) convex optimization framework is modeled based on the echoed data, and least absolute shrinkage and selection operator (Lasso) model and sparse group Lasso (SG-Lasso) model are introduced, then the maximum marginal likelihood distribution of the regularization parameters is derived. Moreover, the Moreau Yoshida unadjusted Langevin algorithm (MYULA) is used to realize target posteriori sampling solution. Because the posterior distribution is difficult to solve, the gradient projection method is introduced to estimate the regularization parameters. Finally, auto-learning parameters are used to optimize the imaging. The proposed algorithm can not only estimate the parameters of a single regularization term, but also estimate the parameters of multiple regularization terms. Aiming at non-differentiable part in the prior, MYULA is adopted to calculate the subgradient of the non-differentiable posterior distribution. Therefore, the proposed algorithm is capable of auto-leaning parameters even regularization function is non-differentiable. In the experimental part, compared with the optimal value of manual debugging, the error between the proposed method and the optimal value is within 15%, and the effectiveness of the algorithm are verified by phase transition diagram (PTD).