Research article Special Issues

A fuzzy identification method for persistent scatterers in PSInSAR technology

  • Received: 09 July 2020 Accepted: 18 September 2020 Published: 15 October 2020
  • Persistent Scatterer SAR Interferometry (PSInSAR) is known as one of the most effective technique for monitoring and analyzing ground deformation. It is a key step that how to identify Persistent Scatterers (PS) effectively and automatically from time-series SAR images. In the past research, one pixel will be classified to "PS" set or "no-PS" set clearly by one or more threshold rules for PS features. However, it is easy to fall into the 'either this or that' logical paradox in some cases because the covered objects in study area usually possess ambiguous boundary for interested characteristics. In this paper, a fuzzy PS concept is present and a fuzzy identification method is designed based on fuzzy set theory by taking the fuzzy characteristics of the pixels into account. Two groups of real data tests indicate that the new method can not only recognize more effective and reliable PS, but also can obtain the better quality results of selected PS, which can be used to evaluate the result of deformation and further improve the PSInSAR technology.

    Citation: Wanli Liu, Qiuzhao Zhang, Yueying Zhao. A fuzzy identification method for persistent scatterers in PSInSAR technology[J]. Mathematical Biosciences and Engineering, 2020, 17(6): 6928-6944. doi: 10.3934/mbe.2020358

    Related Papers:

  • Persistent Scatterer SAR Interferometry (PSInSAR) is known as one of the most effective technique for monitoring and analyzing ground deformation. It is a key step that how to identify Persistent Scatterers (PS) effectively and automatically from time-series SAR images. In the past research, one pixel will be classified to "PS" set or "no-PS" set clearly by one or more threshold rules for PS features. However, it is easy to fall into the 'either this or that' logical paradox in some cases because the covered objects in study area usually possess ambiguous boundary for interested characteristics. In this paper, a fuzzy PS concept is present and a fuzzy identification method is designed based on fuzzy set theory by taking the fuzzy characteristics of the pixels into account. Two groups of real data tests indicate that the new method can not only recognize more effective and reliable PS, but also can obtain the better quality results of selected PS, which can be used to evaluate the result of deformation and further improve the PSInSAR technology.
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    [1] R. M. Goldstein, C. L. Werner, Radar interferogram filtering for geophysical applications, Geophys. Res. Lett., 25 (1998), 4035-4038.
    [2] T. Strozzi, U. Wegmuller, H. R. Keusen, K. Graf, A. Wiesmann, Analysis of the terrain displacement along a funicular by SAR interferometry, IEEE Geosci. Remote Sens. Lett., 3 (2006), 15-18
    [3] D. Raucoules, C. Colesanti, C. Carnec, Use of SAR interferometry for detecting and assessing ground subsidence, C. R. Geosci., 339 (2007), 289-302
    [4] D. Li, M. Liao, Y. Wang, Progress of Permanent Scatterer Interferometry (in Chinese), Editorial Board Geomatics Inf. Sci. Wuhan Univ., 29 (2004), 664-668
    [5] G. Liu, X. Luo, Q. Chen, D. Huang, X. Ding, Detecting land subsidence in Shanghai by PS-networking SAR interferometry, Sensors, 8 (2008), 4725-4741
    [6] B. Osmanoğlu, T. H. Dixon, S. Wdowinski, E. Cabral-Cano, Y. Jiang, Mexico City subsidence observed with persistent scatterer InSAR, Int. J. Appl. Earth Obs. Geoinf., 13 (2011), 1-12.
    [7] C. Colesanti, S. Le Mouelic, M. Bennani, D. Raucoules, C. Carnec, A. Ferretti, Detection of mining related ground instabilities using the Permanent Scatterers Technique-A case study in the east of France, Int. J. Remote Sens., 26 (2007), 201-207
    [8] S. Heleno, L. G. S. Oliveira, M. J. Henriques, A. P. Falcã o, J. Lima, G. Cooksley, et al., Persistent scatterers interferometry detects and measures ground subsidence in Lisbon, Remote Sens. Environ., 115 (2011), 2152-2167
    [9] V. Tofani, F. Raspini, F. Catani, N. Casagli, Persistent Scatterer Interferometry (PSI) technique for landslide characterization and monitoring, Remote Sens., 5 (2013), 1045-1065.
    [10] Z. Sadeghi, M. Zoej, J. P. Muller, Combination of Persistent Scatterer Interferometry and Single-Baseline Polarimetric Coherence Optimisation to Estimate Deformation Rates with Application to Tehran Basin, PFG J. Photogramm. Remote Sens. Geoinf. Sci., 85 (2017), 327-340.
    [11] K. Shirani, M. Pasandi, Detecting and monitoring of landslides using persistent scattering synthetic aperture radar interferometry, Environ. Earth Sci., 78 (2019), 1-24.
    [12] M. Youm, T. M. Timothy, S. Lee, H. Kim, Displacement Measuring of Coastal Area using PS-InSAR, J. Coastal Res., 91 (2019), 291-295.
    [13] A. Hopper, Persistent Scatterer Radar Interferometry for Crustal Deformation Studies and Modeling of Volcanic Deformation, Dissertation: Stanford University, 2005.
    [14] B. Kampes, Displacement Parameter Estimation using Permanent Scatterer Interferometry, Dissertation Delft University of technology, 2005.
    [15] A. Ferretti, C. Prati, F. Rocca, Nonlinear subsidence rate estimation using Permanent Scatterers in differential SAR interferometry, IEEE Trans. Geosci. Remote Sens., 38 (2000), 2202-2212.
    [16] A. Ferretti, C. Prati, F. Rocca, Permanent Scatterers in SAR interferometry, IEEE Trans. Geosci. Remote Sens., 39 (2001), 8-20
    [17] Q. Chen, Y. Li, G. Liu, Comparison and Evaluation of Identification Methods of Permanent Scatterers in Radar Interferometry (in Chinese), Remote Sens. Inf., 4 (2006), 21-23
    [18] Q. Chen, G. Liu, Y. Li, X. Ding, Automated Detection of Permanent Scatterers in Radar Interferometry: Algorithm and Testing Results (in Chinese), Acta Geod. Cartographica Sin., 35 (2006), 112-117
    [19] B. Hu, H. Wang, L. Jia, Automatic detection of permanent scatteres in PSInSAR (in Chinese), Sci. Surv. Mapp., 36 (2011), 50-52
    [20] B. Kampes, N. Adam, Velocity field retrieval from long term coherent points in radar interferometric stacks, Proceedings of International Geoscience and Remote Sensing Symposium, IEEE cat. No. IGARSS2003, 2003.
    [21] B. Kampes, R. Hanssen, Ambiguity resolution for permanent scatterer interferometry, IEEE Trans. Geosci. Remote Sens., 42 (2004), 2446-2453.
    [22] A. Hooper, H. Zebker, P. Segall, B. Kampes, A new method for measuring deformation on volcanoes and other natural terrains using InSAR persistent scatterers, Geophys. Res. Lett., 31 (2004), 1-5.
    [23] A. Hooper, P. Segall, H. Zebker, Persistent scatterer interferomet ric synthetic aperture radar for crustal deformation analysis, with application to Volcan Alcedo, Galapagos, J. Geophys. Res., 112 (2007), 400-407.
    [24] X. Luo, D. Huang, G. Liu, Automated Detection of Permanent Scatterers in Time Serial Differential Radar Interferometry (in Chinese), J. Southwest Jiaotong Univ., 42 (2007), 414-418
    [25] S. Azadnejad, Y. Maghsoudi, D. Perissin, Evaluation of polarimetric capabilities of dual polarized Sentinel-1 and TerraSAR-X data to improve the PSInSAR algorithm using amplitude dispersion index optimization, Int. J. Appl. Earth Obs. Geoinf., 84 (2020), 191950.
    [26] S. Long, T. Li, T. Feng, Study on Selection of PS point Targets (in Chinese), J. Geod. Geodyn., 31 (2011), 144-148.
    [27] Q. Tao, G. Liu, Identification and Selection of Persistent Scatterer Pixels from SAR Images, J. Appl. Scien., 27 (2009), 508-513.
    [28] A. Zadeh, Fuzzy sets, Inf. Control, 8 (1956), 338-353.
    [29] P. Balasubramaniam, V. P. Ananthi, Image fusion using intuitionistic fuzzy sets, Inf. Fusion, 20 (2014), 21-30.
    [30] P. Biswas, B. B. Pal, A fuzzy goal programming method to solve congestion management problem using genetic algorithm, Decis. Making Appl. Manage. Eng., 2 (2019), 36-53.
    [31] S. Ganguly, Multi-objective distributed generation penetration planning with load model using particle swarm optimization, Decis. Making Appl. Manage. Eng., 3 (2020), 30-42.
    [32] G. R. Sensing, Gamma User's Guide, Differential Interferometry and Geocoding Software Version 1.2, GAMMA Remote Sensing AG, 2008.
    [33] X. Song, Fuzzy math theory and method, China Mining University Press, Xuzhou, China, 1999.
    [34] A. Lotfi, C. Tsoi, Learning fuzzy inference systems using an adaptive membership function scheme, IEEE Trans. Syst. Man Cybern. Part B, 26 (1996), 326-331.
    [35] Q. Su, L. Lai, P. Austin, A fuzzy dissolved gas analysis method for the diagnosis of multiple incipient faults in a transformer, APSCOM 2000-5th International Conference on Advances in Power System Control, Operation and Management, 2000.

    © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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