Dual-polarization Sentinel-1 data polarization time series InSAR technology surface deformation monitoring—Taking shanghai pudong airport as an example

  • role: First author第一作者
  • Affiliation:

    School of Environment Science and Spatial Information, China University of Mining and Technology, Xuzhou 221116, China

    National Administration of Surveying Mapping and Geo-Information (NASG), Key Laboratory of Land Environment and Disaster Monitoring, Xuzhou 221116, China

  • Email:Hanfeng709@126.com
  • Introduction:InSARE-mailHanfeng709@126.com
FENG Han12,  
  • Affiliation:

    School of Environment Science and Spatial Information, China University of Mining and Technology, Xuzhou 221116, China

    National Administration of Surveying Mapping and Geo-Information (NASG), Key Laboratory of Land Environment and Disaster Monitoring, Xuzhou 221116, China

ZHAO Feng12,  
  • role: Corresponding author通信作者
  • Affiliation:

    School of Environment Science and Spatial Information, China University of Mining and Technology, Xuzhou 221116, China

    National Administration of Surveying Mapping and Geo-Information (NASG), Key Laboratory of Land Environment and Disaster Monitoring, Xuzhou 221116, China

  • Email:wyj4139@163.com
  • Introduction:E-mailwyj4139@163.com
WANG Yunjia12*,  
  • Affiliation:

    School of Environment Science and Spatial Information, China University of Mining and Technology, Xuzhou 221116, China

    National Administration of Surveying Mapping and Geo-Information (NASG), Key Laboratory of Land Environment and Disaster Monitoring, Xuzhou 221116, China

YAN Shiyong12,  
  • Affiliation:

    School of Environment Science and Spatial Information, China University of Mining and Technology, Xuzhou 221116, China

    National Administration of Surveying Mapping and Geo-Information (NASG), Key Laboratory of Land Environment and Disaster Monitoring, Xuzhou 221116, China

PENG Kai12,  
  • Affiliation:

    School of Environment Science and Spatial Information, China University of Mining and Technology, Xuzhou 221116, China

    National Administration of Surveying Mapping and Geo-Information (NASG), Key Laboratory of Land Environment and Disaster Monitoring, Xuzhou 221116, China

WANG Teng12,  
  • Affiliation:

    School of Environment Science and Spatial Information, China University of Mining and Technology, Xuzhou 221116, China

    National Administration of Surveying Mapping and Geo-Information (NASG), Key Laboratory of Land Environment and Disaster Monitoring, Xuzhou 221116, China

ZHANG Nianbin12,  
  • Affiliation:

    School of Environment Science and Spatial Information, China University of Mining and Technology, Xuzhou 221116, China

    Yellow River Engineering Consulting Co., Ltd., Zhengzhou 450003, China

XU Dongbiao13

résumé

Synthetic Aperture Radar (SAR) data from different polarization channels have distinct responses to ground objects. The surface deformation monitoring capabilities of the conventional single-polarization InSAR technology can be improved by using multi-polarization SAR data. Existing studies have compared the deformation monitoring capabilities of different polarization data (VV-VH) for dual-polarization Sentinel-1 data or optimized a certain scattering target based on a single quality criterion to improve its interference phase quality, which is supposed to have better performance than conventional persistent scatterer interferometry (PSI) approaches. However, the potential of dual-polarized Sentinel-1 data was not fully realized. To this end, this work proposes an adaptive polarimetric persistent scatterer interferometry method (PolPSI) based on dual-polarization Sentinel-1 data. In the PolPSI method, the persistent scatterer target and distributed scattering target are adaptively optimized to obtain optimized interferograms, and they are used as bases to monitor surface deformation. Besides, in this study, we simultaneously applies PSI, DSI, and PolPSI method for Shanghai Pudong International Airport deformation monitoring, by using 34 scenes of dual-polarization (VV-VH) Sentinel-1 images, and combining the information from both VV and VH channels, the polarimetric persistent scatterer interferometry (PolPSI) techniques is supposed to achieve better ground deformation monitoring results than conventional PSI techniques (using only VV channel) and DSI techniques (using MMSE polarization filtering). Based on results obtained, the different characteristics of PolPSI techniques have been discussed. The results show that the use of the PolPSI algorithm can effectively improve the interferometric phase quality of scatterers. Thus, more qualified pixels can be used for ground deformation estimation by PolPSI methods with respect to the PSI technique and the DSI techniques. Specifically, in comparison with the conventional PSI and DSI technologies, the densities of the monitored pixels obtained by the PolPSI technology in this work increased by 103% and 30.8%, respectively, which can better invert the deformation in some local parts of the airport area. What’s more, PSI, and DSI, and PolPSI, the three types of InSAR techniques’ monitoring deformation is consistent. Indicate that the monitoring result of PolPSI is reliable, and it’s the most efficient method among these three methods. On the other hand, all scatterers’ optimal scattering parameter histograms show that PolPSI is the first choice for the area with abundant deterministic scatterers. Therefore, the PolPSI method based on dual-polarization Sentinel-1 data proposed in this work can improve the ability of Sentinel-1 data in the application of surface deformation monitoring by utilizing and mining polarization information.

mots-clés

remote sensing;dual-polarization Sentinel-1 SAR images;ground deformation monitoring;time-series InSAR;interferogram polarimetric optimization

References

  1. 1.
    Azadnejad S, Maghsoudi Y and Perissin D. 2020. Evaluation of polarimetric capabilities of dual polarized Sentinel-1 and TerraSAR-X data to improve the PSInSAR algorithm using amplitude dispersion index optimization. International Journal of Applied Earth Observation and Geoinformation, 84: 101950
  2. 2.
    Berardino P, Fornaro G, Lanari R and Sansosti E. 2002. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Transactions on Geoscience and Remote Sensing, 40(11): 2375-2383
  3. 3.
    Cao N, Lee H and Jung H C. 2015. Mathematical framework for Phase-Triangulation algorithms in distributed scatterer interferometry. IEEE Geoscience & Remote Sensing Letters, 12(9): 1-5
  4. 4.
    Cloude S R and Papathanassiou K P. 1998. Polarimetric SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 36(5): 1551-1565
  5. 5.
    Crosetto M, Monserrat O, Cuevas-González M, Devanthéry N and Crippa B. 2016. Persistent scatterer interferometry: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 115: 78-89
  6. 6.
    Ferretti A, Fumagalli A, Novali F, Prati C, Rocca F and Rucci A. 2011. A new algorithm for processing interferometric data-stacks: SqueeSAR. IEEE Transactions on Geoscience and Remote Sensing, 49(9): 3460-3470
  7. 7.
    Ferretti A, Prati C and Rocca F. 2001. Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 39(1): 8-20
  8. 8.
    Fornaro G, Verde S, Reale D and Pauciullo A. 2015. CAESAR: an approach based on covariance matrix decomposition to improve multibaseline-multitemporal interferometric SAR processing. IEEE Transactions on Geoscience and Remote Sensing, 53(4): 2050-2065
  9. 9.
    Guo H and Zhang L. 2019. 60 Years of radar remote sensing: four-stage development. Journal of Remote Sensing, 23(6):1023-1035
  10. 10.
    Hooper A, Zebker H, Segall P and Kampes B. 2004. A new method for measuring deformation on volcanoes and other natural terrains using InSAR persistent scatterers. Geophysical Research Letters, 31(23): L23611
  11. 11.
    Iglesias R, Monells D, Fabregas X, Mallorqui J J, Aguasca A and Lopez-Martinez C. 2014. Phase quality optimization in polarimetric differential SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 52(5): 2875-2888
  12. 12.
    Jiang M, Miao Z L, Gamba P and Yong B. 2017. Application of multitemporal InSAR covariance and information fusion to robust road extraction. IEEE Transactions on Geoscience and Remote Sensing, 55(6): 3611-3622
  13. 13.
    Lee J S, Grunes M R and de Grandi G. 1999. Polarimetric SAR speckle filtering and its implication for classification. IEEE Transactions on Geoscience and Remote Sensing, 37(5): 2363-2373
  14. 14.
    Lee J S and Pottier E. 2009. Polarimetric Radar Imaging: From Basics to Applications. Boca Raton, FL: CRC Press
  15. 15.
    Liao M S, Wang R, Yang M S, Wang N, Qin X Q and Yang T L. 2020. Techniques and applications of spaceborne time-series InSAR in urban dynamic monitoring. Journal of Radars, 9(3): 409-424
  16. 16.
    Lin H, Ma P, Wang W. 2017. Urban infrastructure health monitoring with spaceborne multi-temporal synthetic aperture radar interferometry. Acta Geodaetica et Cartographica Sinica, 46(10): 1421-1433
  17. 17.
    Liu D M. 2018. Settlement Deformation Characteristics of Soft Soil and Its Control Measures on the Fourth Runway of Pudong Airport. Beijing: China University of Mining and Technology (刘冬明. 2018. 浦东机场四跑道软弱土沉降变形特性及其控制对策. 北京: 中国矿业大学) [DOI: 10.27624/d.cnki.gzkbu.2018.000013]
  18. 18.
    Navarro-Sanchez V D, Lopez-Sanchez J M and Vicente-Guijalba F. 2010. A contribution of polarimetry to satellite differential SAR interferometry: increasing the number of pixel candidates. IEEE Geoscience and Remote Sensing Letters, 7(2): 276-280
  19. 19.
    Navarro-Sanchez V D and Lopez-Sanchez J M. 2014. Spatial adaptive speckle filtering driven by temporal polarimetric statistics and its application to PSI. IEEE Transactions on Geoscience and Remote Sensing, 52(8): 4548-4557
  20. 20.
    Parizzi A and Brcic R. 2011. Adaptive InSAR stack multilooking exploiting amplitude statistics: a comparison between different techniques and practical results. IEEE Geoscience and Remote Sensing Letters, 8(3): 441-445
  21. 21.
    Pipia L, Fabregas X, Aguasca A, Lopez-Martinez C and Mallorquí J J. 2009. Polarimetric coherence optimization for interferometric differential applications//2009 IEEE International Geoscience and Remote Sensing Symposium. Cape Town: IEEE: V-146-V-149
  22. 22.
    Shamshiri R, Nahavandchi H and Motagh M. 2018. Persistent scatterer analysis using dual-polarization sentinel-1 data: contribution from VH channel. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(9): 3105-3112
  23. 23.
    Wang Y Y, Zhu X X and Bamler R. 2012. Retrieval of phase history parameters from distributed scatterers in urban areas using very high resolution SAR data. ISPRS Journal of Photogrammetry and Remote Sensing, 73: 89-99
  24. 24.
    Werner C, Wegmuller U, Strozzi T and Wiesmann A. 2003a. Interferometric point target analysis for deformation mapping//2003 IEEE International Geoscience and Remote Sensing Symposium. Toulouse: IEEE: 4362-4364
  25. 25.
    Werner C, Wegmuller U, Wiesmann A and Strozzi T. 2003b. Interferometric point target analysis with JERS-1 l-band SAR data//2003 IEEE International Geoscience and Remote Sensing Symposium. Toulouse: IEEE: 4359-4361
  26. 26.
    Xiong J C, Nie Y J, Luo Y and Li Y F. 2019. Monitoring urban land subsidence by dual-polarization Sentinel-1 data: a case study of Shanghai. Bulletin of Surveying and Mapping, 11: 98-102, 129
  27. 27.
    Zhang L, Ding X L and Lu Z. 2011. Ground settlement monitoring based on temporarily coherent points between two SAR acquisitions. ISPRS Journal of Photogrammetry and Remote Sensing, 66(1): 146-152
  28. 28.
    Zhao F and Mallorqui J J. 2019a. SMF-POLOPT: an adaptive multitemporal Pol(DIn)SAR filtering and phase optimization algorithm for PSI applications. IEEE Transactions on Geoscience and Remote Sensing, 57(9): 7135-7147
  29. 29.
    Zhao F and Mallorqui J J. 2019b. Coherency matrix decomposition-based polarimetric persistent scatterer interferometry. IEEE Transactions on Geoscience and Remote Sensing, 57(10): 7819-7831
  30. 30.
    Zhao F and Mallorqui J J. 2019c. A temporal phase coherence estimation algorithm and its application on DInSAR pixel selection. IEEE Transactions on Geoscience and Remote Sensing, 57(11): 8350-8361
  31. 31.
    Zhu J J, Yang Z F and Li Z W.2019.Recent progress in r etrieving and predicting mining-induced 3D displaceme nts using InSAR. Acta Geodaetica et Cartographica Si nica,48(2):135-144

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