IEEE Geoscience and Remote Sensing Magazine - March 2020 - 37

great potential and value in the application of surface deformation analysis.
In this article, we presented a comprehensive review of
the current time-series InSAR technique and its applications.
The time-series InSAR technique consists of a series of key
steps: PS and/or DS identification, interferograms generation,
phase unwrapping, reference network construction, parameter estimation, and so on. Based on these steps, we classified
and summarized the dozens of different time-series InSAR
processing frameworks. The processing chain of nearly all
current methods refers to these processing modules. When
a new time-series InSAR processing method is studied, these
perspectives facilitate understanding. The identification and
utilization of various stable scatterers provide the basis of all
time-series InSAR methods. Whether PS, DS, TCP, quasi-PS
(QPS) and so on, identifying sufficient stable targets with a
high SNR is the premise for effective signal extraction.
Phase unwrapping and parameter estimation are the
most important topics in the framework of time-series
InSAR processing, and most studies are focused on these
aspects. To make full use of 3D data information, phase unwrapping is developed in a multibaseline-based approach
to achieve more robust estimation. It combines spatial
baseline and temporal information and relies not only the
assumption of phase continuity. Some methods establish
2D signal models and perform parameter estimation. At
the same time, phase unwrapping can be accomplished according to the spatial statistical characteristics of the signal.
That is to say, the relationship between phase unwrapping
and parameter estimation becomes more and more closely
linked, rather than being two independent parts.
In terms of its applications, the time-series InSAR method has been verified in many fields, such as urban subsidence, infrastructure deformation monitoring, landslides,
earthquakes, and volcanoes. Undoubtedly, these phenomena are reflected in the slow deformation of the corresponding surface objects. The difference in specific application
scenarios drives us to choose different processing methods; therefore, in specific applications, the band, scale, and
resolution of data must be considered. Current time-series
InSAR techniques still have a strong dependence on data
quality. The quality and appropriateness of the data determine the upper limit of the effect of the final result. And
the choice of treatment method determines the degree to
which the upper limit is approached.
Additionally, with the accumulation of SAR data, the development of technology, and the improvement of processing ability, time-series InSAR may be further promoted. Of
course, for large-scene processing, atmospheric correction,
multidimensional deformation, and other issues, further
research is needed to make time-series InSAR more reliable
and robust.
ACKNOWLEDGMENTS
This work was supported by the Hundred Talents Program
(grant Y53Z180390), National Key R&D Program of China
MARCH 2020

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE

(grant 2018YFC1505101), and National Natural Science
Foundation of China (grant 41801356).
AUTHOR INFORMATION
Feiyang Xue (xuefeiyang16@mails.ucas.ac.cn) received his
B.S. degree from the School of Electronic Engineering at the
University of Electronic Science and Technology of China,
Chengdu, in 2016. He is currently a Ph.D. candidate at the
Key Laboratory of Technology in Geo-spatial Information
Processing and Application Systems, Institute of Electronics of the Chinese Academy of Sciences, Beijing, where he is
researching interferometric synthetic aperture radar technology and its applications.
Xiaolei Lv (academism2017@sina.com) received his
B.S. degree from the School of Computer Science and
Technology at Xidian University, Xi'an, China in 2004 and
his Ph.D. degree from the National Laboratory of Radar
Signal Processing at Xidian University, in 2009. Currently,
he is a professor and fellow with the Hundred Talents Program at the Institute of Electronics of the Chinese Academy of Sciences, Beijing. He has had more than 40 research
papers published in journals such as IEEE Transactions on
Geoscience and Remote Sensing, IEEE Transactions on Signal
Processing, IEEE Transactions on Image Processing, and IEEE
Geoscience and Remote Sensing Letters. His research interests
include radar imaging, target detection, microwave remote sensing, and array signal processing. He is a Member
of the IEEE.
Fangjia Dou (doufangjia16@mails.ucas.ac.cn) received her
B.S. degree from the School of Electronic Engineering at the
University of Electronic Science and Technology of China,
Chengdu, in 2016. She is currently a Ph.D. candidate at the
Institute of Electronics of the Chinese Academy of Sciences,
Beijing, where she is researching interferometric synthetic aperture radar deformation analysis and atmospheric effects.
Ye Yun (yunye@aircas.ac.cn) received her B.S. degree
in geography from East China Normal University, Shanghai, in 2010 and her Ph.D. degree in photogrammetry and
remote sensing from Peking University, Beijing, China, in
2015. From 2013 to 2014, she was a visiting scholar with
the Department of Meteorology, the Pennsylvania State
University, University Park. She is currently with the Institute of Electronics, the Chinese Academy of Sciences,
Beijing. Her research interests include interferometric synthetic aperture radar atmospheric correction, processing,
and applications.
REFERENCES
[1] R. Bamler and P. Hartl, "Synthetic aperture radar interferometry," Inverse Probl., vol. 14, no. 4, pp. 12-13, 1999.
[2] P. A. Rosen et al., "Synthetic aperture radar interferometry,"
Proc. IEEE, vol. 88, no. 3, pp. 333-382, 2002.
[3] R. Bürgmann, P. A. Rosen, and E. J. Fielding, "Synthetic aperture radar interferometry to measure Earth's surface topography and its deformation," Annu. Rev. Earth Planetary Sci., vol.
28, no. 1, pp. 169-209, 2000.

IEEE Geoscience and Remote Sensing Magazine - March 2020

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