摘要
提出了一种基于字典学习的干涉合成孔径雷达相位降噪算法.首先利用字典学习,建立了干涉相位滤波的优化模型.鉴于该模型非凸难以求解,采用分裂技术和增广拉格朗日框架,获得松弛后的基于l1范数正则化的优化模型,然后引入交替方向乘子法对松弛后的问题求解,获得最终的相位滤波结果.通过InSAR复相位数据训练字典,从稀疏表达式重建所需的复相位图像.对仿真数据和实测数据的处理显示这种新的InSAR相位降噪方法在残点数、均方误差和边缘完整性保持方面优于现有的经典滤波方法.
We consider the phase noise filtering problem for interferometric synthetic aperture radar（InSAR）based on the dictionary learning technique.Due to the non-convexity of the optimization problem is difficult to solve.By using the splitting technique and employing the augmented Lagrangian framework,we obtain a relaxed nonlinear constraint optimization problem with l_1-norm regularization which can be solved efficiently by the alternating direction method of multipliers（ADMM）.Specifically,we firstly train dictionaries from the InSAR complex phase data,and then reconstruct the desired complex phase image from the sparse representation.Simulation results based on simulated and measured data show that this new InSAR phase noise reduction method has a much better performance than several classical phase filtering methods in terms of residual count,mean square error（MSE）and preservation of the fringe completeness.
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2016年第1期18-23,共6页
Journal of Xidian University
基金
国家自然科学基金资助项目(61362001
51165033)
关键词
INSAR
相位降噪
字典学习
l1范数正则化
交替方向乘子法
interferometric synthetic aperture radar
phase noise reduction
dictionary learning
l1-norm regularization
alternating directional method of multipliers
