基于MC_RTV正则化的高分辨SAR特征增强算法

Feature Enhancement Algorithm for High-Resolution SAR Based on MC and RTV Regularization

针对合成孔径雷达(synthetic aperture radar,SAR)稀疏成像中目标反射率易低估、目标结构特征难以精确提取的问题,提出一种基于非凸和相对全变分(relative total variation,RTV)正则化的稀疏SAR成像算法。该算法利用非凸惩罚抑制偏差效应、RTV自适应保护图像结构,在交替方向乘子法(alternating direction method of multipliers,ADMM)分布式优化框架下,实现多个正则项的协同优化增强。为更好地提高成像效率和降低内存占用量,利用匹配滤波(match filter,MF)算子构造测量矩阵进行近似观测,并对重建的SAR图像质量进行定量评价。仿真与实测数据处理结果表明,所提方法可有效抑制噪声杂波,在保证空间分辨率的情况下有效提高目标重建精度和辐射分辨率。

Aiming at the issues of underestimating target reflectivity and difficulties in accurately extracting targets structural features in sparse imaging of synthetic aperture radar(SAR),a sparse SAR imaging algorithm based on non-convex and relative total variation(RTV)regularization is proposed.This algorithm leverages non-convex penalties to suppress bias effects and employs RTV for adaptive protection of image structures.Subsequently,under the distributed optimization framework of the alternating direction method of multipliers(ADMM),it achieves coordinated optimization enhancement of multiple regularization terms.Additionally,to further enhance imaging efficiency and reduce memory usage,a measurement matrix constructed with match filter(MF)operators is employed for approximate observations,and the quantitative evaluations of the reconstructed SAR image quality are conducted.Both simulation and real-data processing results demonstrate that this method can effectively suppress noise and clutter,significantly improving target reconstruction accuracy and radiometric resolution without compromising spatial resolution.