J4 ›› 2016, Vol. 43 ›› Issue (1): 18-23.doi: 10.3969/j.issn.1001-2400.2016.01.004

• 研究论文 • 上一篇    下一篇



  1. (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. (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



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


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