西安电子科技大学学报（自然科学版）Issue(1)：18-23,6.DOI:10.3969/j.issn.1001-2400.2016.01.004
采用自适应字典学习的InSAR降噪方法
InSAR noise reduction using adaptive dictionary learning
摘要
Abstract
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 l1-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.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Key words
interferometric synthetic aperture radar/phase noise reduction/dictionary learning/l1-norm regularization/alternating directional method of multipliers分类
信息技术与安全科学引用本文复制引用
罗晓梅,索志勇,刘且根..采用自适应字典学习的InSAR降噪方法[J].西安电子科技大学学报（自然科学版）,2016,(1):18-23,6.基金项目
国家自然科学基金资助项目 （）
