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A new polarimetric phase optimization method based on eigenvalue decomposition and adaptive filtering

  • role： First author第一作者
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China

  • Email：729575242@qq.com
  • Introduction：姜修成，研究方向为合成孔径雷达干涉测量算法研究及其应用。E-mail： 729575242@qq.com
JIANG Xiucheng,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China

  • Email：zhangzj@cug.edu.cn
  • Introduction：张正加，研究方向为合成孔径雷达干涉测量算法研究及在地面沉降、地质灾害和青藏高原冻土环境监测中的应用。E-mail： zhangzj@cug.edu.cn
ZHANG Zhengjia*,  
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China

WANG Mengmeng,  
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China

LIU Xiuguo,  
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China

CHEN Qihao

реферат

The time-series interferometric synthetic aperture radar (InSAR) technology based on distributed scatterers (DSs) makes up for the drawback of the persistent scatterer InSAR technology, which struggles to obtain a sufficient number of deformation monitoring points in low-coherence regions. Given that DS targets are easily affected by temporal and spatial decorrelation factors, leading to the introduction of speckle noise, the phase of DS targets must be optimized. In this paper, a new polarimetric phase optimization method based on eigenvalue decomposition and adaptive filtering (EVD-FPO) is proposed to address these limitations. This method uses Sentinel-1A dual-polarization data amplitude information to identify DS and PS targets and uses eigenvalue decomposition polarimetric phase optimization technology based on the temporal and spatial coherence matrix and adaptive mean filtering technology to improve the phase quality. To prove the feasibility and effectiveness of the proposed method, 17 Sentinel-1A dual-polarization (VV-VH) SAR images are used to evaluate its performance. Experimental results show that the proposed EVD-FPO method can effectively increase the density of coherent target points and improve the phase quality. Compared with the single polarimetric amplitude dispersion method (VV-DA) and amplitude dispersion polarimetric phase optimization method (ESM-DA), EVD-FPO increases the coherence point target by 9.06 and 1.64 times, respectively. Compared with the single-polarization CAESAR method, the PSs extracted are more complete. EVD-FPO can suppress the phase noise of the DSs while protecting the phase of the PSs, and the phase quality is better than those of the ESM-DA and CAESAR methods. To evaluate the optimized phase results, this paper adopts the average phase derivative to evaluate the phase quality of each phase optimization method. As for the global average phase derivative, the variation of phase derivative of the ESM-DA method is 1.22, that of the CAESAR method is 0.998, and that of the EVD-FPO method is 0.974. These values indicate that the interferometric phase quality of EVD-FPO is higher than those of the other two methods. Similar conclusions are obtained for the average phase derivative at the coherence points. The variation of the phase derivative of the ESM-DA method is 1.184, that of the CAESAR method is 0.854, and that of EVD-FPO method is 0.810, which also indicates that the interferometric phase quality of EVD-FPO is higher than those of the two former methods. In addition, the effect of physical scattering mechanism of Sentinel-1 dual-polarization data on improving phase quality is investigated. Results show that the phase contribution of PS targets is mainly from VV polarization, while the phase contribution of DS targets is mainly from VH polarization. In general, the EVD-FPO method proposed in this paper is very meaningful for improving the density and phase quality of coherence points. It holds great potential for deformation monitoring in low-coherence areas where artificial targets are scarce.

ключеви́че слова́

remote sensing;polarimetric phase optimization;eigenvalue decomposition;adaptive filtering;coherence points;InSAR

References

  1. 1.
    Ansari H, De Zan F and Bamler R. 2018. Efficient phase estimation for interferogram stacks. IEEE Transactions on Geoscience and Remote Sensing, 56(7): 4109-4125
  2. 2.
    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
  3. 3.
    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
  4. 4.
    Cao N, Lee H and Jung H C. 2016. A phase-decomposition-based PSInSAR processing method. IEEE Transactions on Geoscience and Remote Sensing, 54(2): 1074-1090
  5. 5.
    Cloude S R and Papathanassiou K P. 1997. Polarimetric optimisation in radar interferometry. Electronics Letters, 33(13): 1176-1178
  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. 1999. Permanent scatterers in SAR interferometry//IEEE 1999 International Geoscience and Remote Sensing Symposium. Hamburg: IEEE: 1528-1530
  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.
    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
  10. 10.
    Ji K F and Wu Y H. 2015. Scattering mechanism extraction by a modified cloude-pottier decomposition for dual polarization SAR. Remote Sensing, 7(6): 7447-7470
  11. 11.
    Jiang M, Ding X L, He X F, Li Z W and Shi G Q. 2016. FaSHPS-InSAR technique for distributed scatterers: a case study over the lost hills oil field, California. Chinese Journal of Geophysics, 59(10): 3592-3603
  12. 12.
    Jiang M and Guarnieri A M. 2020. Distributed scatterer interferometry with the refinement of spatiotemporal coherence. IEEE Transactions on Geoscience and Remote Sensing, 58(6): 3977-3987
  13. 13.
    Liao M S and Wang T. 2014. Time series InSAR technology and applications. Beijing: Science Press
  14. 14.
    Narayan A B, Tiwari A, Dwivedi R and Dikshit O. 2018. A novel measure for categorization and optimal phase history retrieval of distributed scatterers for InSAR applications. IEEE Transactions on Geoscience and Remote Sensing, 56(10): 5843-5849
  15. 15.
    Navarro-Sanchez V D and Lopez-Sanchez J M. 2012. Improvement of persistent-scatterer interferometry performance by means of a polarimetric optimization. IEEE Geoscience and Remote Sensing Letters, 9(4): 609-613
  16. 16.
    Navneet S, Kim J W and Lu Z. 2018. A new InSAR persistent scatterer selection technique using top eigenvalue of coherence matrix. IEEE Transactions on Geoscience and Remote Sensing, 56(4): 1969-1978
  17. 17.
    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
  18. 18.
    Pipia L, Fabregas X, Aguasca A, Lopez-Martinez C, Duque S, Mallorqui J J and Marturia J. 2009. Polarimetric differential SAR interferometry: first results with ground-based measurements. IEEE Geoscience and Remote Sensing Letters, 6(1): 167-171
  19. 19.
    Sadeghi Z, Zoej M J V, Hooper A and Lopez-Sanchez J M. 2018. A new polarimetric persistent scatterer interferometry method using temporal coherence optimization. IEEE Transactions on Geoscience and Remote Sensing, 56(11): 6547-6555
  20. 20.
    Sahraoui O H, Hassaine B, Serief C and Kamel Hasni. 2006. Radar interferometry with Sarscape software//XXIII International FIG Congress: Shaping the Change. Munich: [s.n.]
  21. 21.
    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
  22. 22.
    Wang C, Zhang H and Liu Z. 2002. Spaceborne Synthetic Aperture Radar Interferometry. Beijing: Science Press
  23. 23.
    Zhao C J, Li Z, Tian B S, Zhang P and Chen Q. 2019. A ground surface deformation monitoring InSAR method using improved distributed scatterers phase estimation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(11): 4543-4553
  24. 24.
    Zhao F and Mallorqui J J. 2019a. A temporal phase coherence estimation algorithm and its application on DInSAR pixel selection. IEEE Transactions on Geoscience and Remote Sensing, 57(11): 8350-8361
  25. 25.
    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
  26. 26.
    Zhao F and Mallorqui J J. 2019c. 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

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