采用自适应字典学习的InSAR降噪方法

doi:10.3969/j.issn.1001-2400.2016.01.004

J4 ›› 2016, Vol. 43 ›› Issue (1): 18-23.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

罗晓梅1,2;索志勇3;刘且根2,4

(1. 西安电子科技大学综合业务网理论及关键技术国家重点实验室，陕西西安 710071;
2. 南昌大学信息工程学院，江西南昌 330031;
3. 西安电子科技大学雷达信号处理国家重点实验室，陕西西安 710071;
4. 中国科学院劳特伯生物医学成像研究中心，广东深圳 518055)

收稿日期:2014-08-02 出版日期:2016-02-20 发布日期:2016-04-06
通讯作者: 罗晓梅
作者简介:罗晓梅(1971-)，女，西安电子科技大学博士研究生，E-mail: xxmluo@gmail.com．
基金资助:
国家自然科学基金资助项目(61362001, 51165033)

InSAR noise reduction using adaptive dictionary learning

LUO Xiaomei1,2;SUO Zhiyong3;LIU Qiegen2,4

(1. State Key Lab. of Integrated Service Networks, Xidian Univ., Xi'an 710071, China;
2. Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China;
3. National Key Lab. of Radar Signal Processing, Xidian Univ., Xi'an 710071, China;
4. The Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Key Lab. for MRI, Chinese Academy of Sciences, Shenzhen 518055, China)

Received:2014-08-02 Online:2016-02-20 Published:2016-04-06
Contact: LUO Xiaomei

摘要/Abstract

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．

关键词: InSAR, 相位降噪, 字典学习, l<sub>1</sub>范数正则化, 交替方向乘子法

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.

Key words: interferometric synthetic aperture radar, phase noise reduction, dictionary learning, l<sub>1</sub>-norm regularization, alternating directional method of multipliers

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]. J4, 2016, 43(1): 18-23.

LUO Xiaomei;SUO Zhiyong;LIU Qiegen. InSAR noise reduction using adaptive dictionary learning[J]. J4, 2016, 43(1): 18-23.

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采用自适应字典学习的InSAR降噪方法

InSAR noise reduction using adaptive dictionary learning

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