Comparison of density and positioning accuracy of PS extracted from super-resolution PSI with those from traditional PSI

doi:10.23919/JSEE.2021.000111

Journal of Systems Engineering and Electronics ›› 2021, Vol. 32 ›› Issue (6): 1318-1324.doi: 10.23919/JSEE.2021.000111

• RADAR DIFFERENTIAL INTERFEROMETRY TECHNIQUES AND APPLICATIONS • Previous Articles Next Articles

Comparison of density and positioning accuracy of PS extracted from super-resolution PSI with those from traditional PSI

Hao ZHANG¹(), Bin CUI^1,²(), Zhichao GUAN^3,*(), Han DUN⁴()

¹ School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210049, China
² Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing 100038, China
³ State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
⁴ Department of Geoscience and Remote Sensing, Delft University of Technology, Delft 2628, the Netherlands

Received:2021-05-07 Online:2022-01-05 Published:2022-01-05
Contact: Zhichao GUAN E-mail:haozhang@njupt.edu.cn;bincui@njupt.edu.cn;guanzhichao@whu.edu.cn;h.dun@tudelft.nl
About author:|ZHANG Hao was born in 1986. He received his B.S. in remote sensing and information engineering from Wuhan University in 2010, master’s degree in geodetection and information technology from China University of Geosciences (Wuhan) in 2016, and Ph.D. degree from the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, China in 2020. He was a visiting Ph.D. student with the Department of Geoscience and Remote Sensing, Delft University of Technology, the Netherlands, from 2017 to 2019. Currently, he is a lecturer in Nanjing University of Posts and Telecommunications. His research interests include new processing algorithms for SAR/InSAR. E-mail: haozhang@njupt.edu.cn||CUI Bin was born in 1989. He received his M.S. degree from the School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture in 2015 and Ph.D. degree from the School of Geodesy and Geomatics, Wuhan University, China in 2020. He is currently a lecturer at the School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing. His research interests include remote sensing data processing and deep learning. E-mail: bincui@njupt.edu.cn||GUAN Zhichao was born in 1990. He received his M.S. degree from China University of Geosciences, Wuhan in 2014, and Ph.D. degree from the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, China in 2018. He was engaged in scientific research at the postdoctoral research station, Wuhan University, from 2018 to 2021. His research interests include satellite remote sensing data processing. E-mail: guanzhichao@whu.edu.cn||DUN Han was born in 1991. He received his B.S. degree in communication engineering and M.S. degree in communication and information engineering from Shanghai University, China, in 2013 and 2016, respectively. From 2013 to 2016, he also worked at the Key Laboratory of Specialty Fiber Optics and Optical Access Network in Shanghai University. He is currently pursuing his Ph.D. degree in the Department of Geoscience and Remote Sensing, Delft University of Technology, the Netherlands. His research interests include wireless localization, and statistical signal processing. E-mail: h.dun@tudelft.nl
Supported by:
This work was supported by the National Natural Science Foundation of China (62101284), the State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of Ministry of Natural Resources, China Academy of Surveying and Mapping (2021-03-11), the Natural Science Project of Jiangsu Province (21KJB420003), Nanjing University of Posts and Telecommunications Start-up Fund (NY221033; NY220168), the Foundation of Jiangsu Province Shuangchuang Doctor Grant (JSSCBS20210543), and Beijing Key Laboratory of Urban Spatial Information Engineering (20210215)

Abstract

Abstract:

In the application of persistent scatterer interferometry (PSI), deformation information is extracted from persistent scatterer (PS) points. Thus, the density and position of PS points are critical for PSI. To increase the PS density, a time-series InSAR chain termed as “super-resolution persistent scatterer interferometry” (SR-PSI) is proposed. In this study, we investigate certain important properties of SR-PSI. First, we review the main workflow and dataflow of SR-PSI. It is shown that in the implementation of the Capon algorithm, the diagonal loading (DL) approach should be only used when the condition number of the covariance matrix is sufficiently high to reduce the discontinuities between the joint images. We then discuss the density and positioning accuracy of PS when compared with traditional PSI. The theory and experimental results indicate that SR-PSI can increase the PS density in urban areas. However, it is ineffective for the rural areas, which should be an important consideration for the engineering application of SR-PSI. Furthermore, we validate that the positioning accuracy of PS can be improved by SR-PSI via simulations.

Key words: super resolution, persistent scatterer interferometry (PSI), positioning accuracy

Hao ZHANG, Bin CUI, Zhichao GUAN, Han DUN. Comparison of density and positioning accuracy of PS extracted from super-resolution PSI with those from traditional PSI[J]. Journal of Systems Engineering and Electronics, 2021, 32(6): 1318-1324.

Figures/Tables 7

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Comparison of density and positioning accuracy of PS extracted from super-resolution PSI with those from traditional PSI

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