Mitigation of time-series InSAR turbulent atmospheric phase noise: A review  

作　　者：Zhiwei Li Meng Duan Yunmeng Cao Minzheng Mu Xin He Jianchao Wei 

机构地区：[1]School of Geosciences and Info-Physics,Central South University,Changsha 410083,Hunan,China

出　　处：《Geodesy and Geodynamics》2022年第2期93-103,共11页大地测量与地球动力学（英文版）

基　　金：This work was partly supported by the National Science Fund for Distinguished Young Scholars,grant number 41925016;the National Natural Science Foundation of China,grant number 41804008.

摘　　要：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,sug-gestions for resolving the challenging issues and an outlook for future research are given.

关 键 词：INSAR Turbulent atmospheric phase Variance and covariance matrix Stochastic model Deep learning 

分 类 号：P237[天文地球—摄影测量与遥感]