(Peer-Reviewed) Mitigation of time-series InSAR turbulent atmospheric phase noise: A review
Zhiwei Li 李志伟, Meng Duan 段梦, Yunmeng Cao 曹云梦, Minzheng Mu, Xin He, Jianchao Wei 韦建超
School of Geosciences and Info-Physics, Central South University, Changsha 410083, Hunan, China
中国 湖南 长沙 中南大学地球科学与信息物理学院
Abstract
Synthetic Aperture Radar (SAR) interferometry is one of the most powerful remote sensing tools for ground deformation detection. However, tropospheric delay greatly limits the measurement accuracy of the InSAR technique. While vertically stratified tropospheric delays have been extensively investigated and well tackled, turbulent tropospheric phase noise still remains an intractable issue.
In recent years, great efforts have been made to reduce the influence of turbulent atmospheric delay. This contribution is intended to provide a systematic review of the progress achieved in this field. First, it introduces the physical characteristics of atmospheric signals in interferograms.
Then, a review of the main mitigation algorithms proposed in the literature is provided. In addition, the strengths and weaknesses of each approach are analyzed to provide guidance for choosing a suitable method accordingly. Finally, suggestions for resolving the challenging issues and an outlook for future research are given.
Separation and identification of mixed signal for distributed acoustic sensor using deep learning
Huaxin Gu, Jingming Zhang, Xingwei Chen, Feihong Yu, Deyu Xu, Shuaiqi Liu, Weihao Lin, Xiaobing Shi, Zixing Huang, Xiongji Yang, Qingchang Hu, Liyang Shao
Opto-Electronic Advances
2025-11-25
