Acta Optica Sinica, Volume. 39, Issue 5, 0528006(2019)

Persistent Scatterer Detection Method Based on Empirical Mode Decomposition

Changjun Huang1,2、*, Jiyuan Hu3, and Yafu Yang1
Author Affiliations
  • 1 School of Municipal and Surveying Engineering, Hunan City University, Yiyang, Hunan 413000, China
  • 2 School of Geosciences and Info-Physics, Central South University, Changsha, Hunan 410083, China
  • 3 School of Geodesy and Geomatics, Wuhan University, Wuhan, Hubei 430079, China
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    Figures & Tables(13)
    EMD decomposition results of one-dimensional signal. (a) Original signal with noise; (b) imf1 component; (c) imf2 component; (d) imf3 component; (e) imf4 component; (f) residual trend component
    Schematics of multidirectional decomposition of image. (a) Horizontal direction; (b) left diagonal direction; (c) vertical direction; (d) right diagonal direction
    Scattergram of PSC points after initial selection
    Phase comparison of differential interferograms before and after filtering. (a) Original interferogram with noise; (b) interferogram denoised by EMD; (c) interferogram denoised by improved EMD
    Phase of PSC points before and after filtering. (a) Before filtering; (b) after filtering
    Distribution histogram of PSC points
    Probability density function p(γg) of γg
    Final scatter gram of PS points in study area
    Scattergrams of PS points detected by different methods. (a) Amplitude deviation index threshold method; (b) coherence coefficient threshold method; (c) phase deviation index threshold method; (d) EMD method
    Scattergrams of PS points detected by different methods. (a) Amplitude deviation index threshold method; (b) coherence coefficient threshold method; (c) phase deviation index threshold method; (d) EMD method; (e) proposed method
    • Table 1. Basic parametersfor experimental

      View table

      Table 1. Basic parametersfor experimental

      No.DatePerpendicular baseline /mTime Baseline /dNo.DatePerpendicular baseline /mTime Baseline /d
      12007-08-01218-805102009-12-2310670
      22008-02-27-4-595112010-01-27480105
      32008-07-16429-455122010-03-03146140
      42008-09-2457-385132010-04-07604175
      52008-12-03130-315142010-05-12440210
      62009-03-18794-210152010-06-16492245
      72009-07-01408-105162010-07-2165280
      82009-09-09673-35172010-08-25140315
      92009-10-14--182010-09-29480350
    • Table 2. Probability statistics of PSC points

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      Table 2. Probability statistics of PSC points

      α/γg0.98/0.470.98/0.500.80/0.470.80/0.500.70/0.470.70/0.50
      Probability maximum0.9910.9930.8740.8730.7690.761
      Probability mean value0.9820.9840.8350.8430.7240.727
      Probability minimum0.9850.9800.7880.7780.6760.667
      PSC points over mean probability615043016334430162534301
    • Table 3. Numbers of effective PS points and misjudgment PS points extracted with different detection methods and error probability of different detection methods

      View table

      Table 3. Numbers of effective PS points and misjudgment PS points extracted with different detection methods and error probability of different detection methods

      MethodTotal number of PS pointsNumber of effective PS pointsNumber of misjudgment PS pointsError probability /%
      Amplitude deviation2121803215.09
      Coherence coefficient2011742713.43
      Phase deviation1951722311.79
      EMD138126128.69
      Improved EMD15315300
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    Changjun Huang, Jiyuan Hu, Yafu Yang. Persistent Scatterer Detection Method Based on Empirical Mode Decomposition[J]. Acta Optica Sinica, 2019, 39(5): 0528006

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    Paper Information

    Category: Remote Sensing and Sensors

    Received: Nov. 1, 2018

    Accepted: Feb. 19, 2019

    Published Online: May. 10, 2019

    The Author Email:

    DOI:10.3788/AOS201939.0528006

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