An Improved Coherent Targets Technology for Monitoring Surface Deformation

  • WANG Mingzhou ,
  • LI Tao ,
  • JIANG Liming ,
  • XU Kan ,
  • WU Wenhao
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  • 1. GNSS Center, Wuhan University, Wuhan 430079, China;
    2. Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China

Received date: 2015-03-02

  Revised date: 2015-07-12

  Online published: 2016-01-28

Supported by

The National Natural Science Foundation of China (No. 41274048)

Abstract

How to obtain a more comprehensive collection of coherent points from interferometric datasets and analyze deformation time-series is the difficult and hot spot in current research. An improved coherent targets technology is proposed, which can obtain a more comprehensive and reliable collection of coherent points and improve temporal-spatial resolution and precision of deformation signal. Based on similar backscattering properties of the same ground object, non-parametric hypothesis test is used to extract the homogeneous pixels and nonlocal filter is applied to improve the quality of interferogram. Meanwhile, the systematic phase is removed based on multi-resolution maximum likelihood estimation (MLE) of fringes algorithm, then the coherence of homogeneous pixels is estimated stationarily. Thereby more coherent points can be obtained. 20 TerraSAR-X stripmap images are exploited by the conventional CT and the proposed method to investigate the ground deformation of the reclaimed lands in Hong Kong. Experimental results show that this method can effectively improve the density of coherent points in the reclaimed lands with sparse vegetation and obtain a more reliable result.

Cite this article

WANG Mingzhou , LI Tao , JIANG Liming , XU Kan , WU Wenhao . An Improved Coherent Targets Technology for Monitoring Surface Deformation[J]. Acta Geodaetica et Cartographica Sinica, 2016 , 45(1) : 36 -43 . DOI: 10.11947/j.AGCS.2016.20140617

References

[1] FERRETTI A, PRATI C, ROCCA F. Nonlinear Subsidence Rate Estimation Using Permanent Scatterers in Differential SAR Interferometry[J]. IEEE Transactions on Geosciences and Remote Sensing, 2000, 38(5): 2202-2212.
[2] FERRETTI A, PRATI C, ROCCA F. Permanent Scatterers in SAR Interferometry[J].IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(1): 8-20.
[3] MORA O, MALLORQUI J J, BROQUETAS A. Linear and Nonlinear Terrain Deformation Maps from a Reduced Set of Interferometric SAR Images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(10): 2243-2252.
[4] HOOPER A. A Multi-temporal InSAR Method Incorporating both Persistent Scatterer and Small Baseline Approaches[J]. Geophysical Research Letters, 2008, 35(16): L16302.
[5] PERISSIN D, WANG Teng. Repeat-pass SAR Interferometry with Partially Coherent Targets[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(1): 271-280.
[6] 张永红, 张继贤, 龚文瑜, 等. 基于SAR干涉点目标分析技术的城市地表形变监测[J]. 测绘学报, 2009, 38(6): 482-487, 493. ZHANG Yonghong,ZHANG Jixian, GONG Wenyu, et al. Monitoring Urban Subsidence Based on SAR Interferometric Point Target Analysis[J]. Acta Geodaetica et Cartographica Sinica, 2009, 38(6): 482-487, 493.
[7] GOLDSTEIN R M, WERNER C L. Radar Interferogram Filtering for Geophysical Application[J]. Geophysical Research Letters, 1998, 25(21), 4035-4038.
[8] DELEDALLE C A, DENIS L, TUPIN F. Iterative Weighted Maximum Likelihood Denoising with Probabilistic Patch-based Weights[J]. IEEE Transactions on Image Processing, 2009, 18(12): 2661-2672.
[9] DELEDALLE C A, DENIS L, TUPIN F. NL-InSAR: Nonlocal Interferogram Estimation[J]. IEEE Transactions on Geoscience and Remote Sensing,2011, 49(4): 1441-1452.
[10] FERRETTI A, FUMAGALLI A, NOVALI F, et al. A New Algorithm for Processing Interferometric Data-stacks: SqueeSAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(9): 3460-3470.
[11] GOELK, ADAMN. An Advanced Algorithm for Deformation Estimation in Non-urban Areas[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2012, 73: 100-110.
[12] 夏耶. 干涉雷达滑坡监测关键技术探讨[C]//中国地球物理学会第29届学术研讨会论文集.昆明: [s.n.], 2013. XIA Y. Study on InSAR Technology Key Methodology for Landslide Monitoring[C]//Proceedings of the 29th Symposium of Chinese Geophysical Society. Kunming:[s.n.],2013.
[13] LI Jinwei, LI Zhenfang, BAO Zheng, et al. Noise Filtering of High-resolution Interferograms over Vegetation and Urban Areas with a Refined Nonlocal Filter[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(1): 77-81.
[14] TOUZI R, LOPES A, BRUNIQUEL J, et al. Coherence Estimation for SAR Imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(1): 135-149.
[15] GUARNIERI A M, PRATI C. SAR Interferometry: A “Quick and Dirty” Coherence Estimator for Data Browsing[J]. IEEE Transactions on Geoscience and Remote Sensing,1997, 35(3): 660-669.
[16] LEE J S, CLOUDE S R,PAPATHANASSIOU K P,et al. Speckle Filtering and Coherence Estimation of Polarimetric SAR Interferometry Data for Forest Applications[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(10): 2254-2263.
[17] ZEBKER H A,CHEN K.Accurate Estimation of Correlation in InSAR Observations[J]. IEEE Geoscience and Remote Sensing Letters, 2005, 2(2): 124-127.
[18] 蒋弥, 丁晓利, 李志伟, 等.基于时间序列的InSAR相干性量级估计[J]. 地球物理学报, 2013, 56(3): 799-811. JIANG Mi,DING Xiaoli,LI Zhiwei, et al. InSAR Coherence Magnitude Estimation Based on Data Stack[J]. Chinese Journal of Geophysics, 2013, 56(3): 799-811.
[19] STEPHENS M A. Use of the Kolmogorov-Smirnov, Cramér-Von Mises and Related Statistics without Extensive Tables[J]. Journal of the Royal Statistical Society, Series B (Methodological), 1970, 32(1): 115-122.
[20] SCHMITT M, SCHONBERGER J L, STILLA U. Adaptive Covariance Matrix Estimation for Multi-baseline InSAR Data Stacks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(11): 6807-6817.
[21] JIANG Mi, DING Xiaoli, HANSSEN R F, et al. Fast Statistically Homogeneous Pixel Selection for Covariance Matrix Estimation for Multitemporal InSAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(3): 1213-1224.
[22] WANG Yuanyuan, ZHU Xiaoxiang, BAMLER R. Retrieval of Phase History Parameters from Distributed Scatterers in Urban Areas Using Very High Resolution SAR Data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 73: 89-99.
[23] ZEBKER H A, VILLASENOR J. Decorrelation in Interferometric Radar Echoes[J]. IEEE Transactions on Geoscience and Remote Sensing, 1992, 30(5): 950-959.
[24] BUADES A, COLL B, MOREL J M. A Non-local Algorithm for Image Denoising[C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA:IEEE, 2005, 2: 60-65.
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