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.
Feature Enhancement Algorithm for High-Resolution SAR Based on MC and RTV Regularization
Aiming at the issues of underestimating target reflectivity and difficulties in accurately ex-tracting targets structural features in sparse imaging of synthetic aperture radar(SAR),a sparse SAR im-aging 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 direc-tion method of multipliers(ADMM),it achieves coordinated optimization enhancement of multiple regu-larization terms.Additionally,to further enhance imaging efficiency and reduce memory usage,a meas-urement 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.
synthetic aperture radar(SAR)non-convex regularizationrelative total variation(RTV)feature synergistic enhancement
万方数据
维普
