• DocumentCode
    677542
  • Title

    New algorithm for InSAR stack phase triangulation using integer least squares estimation

  • Author

    Samiei-Esfahany, Sami ; Hanssen, Ramon F.

  • Author_Institution
    Dept. of Geosci. & Remote Sensing, Delft Univ. of Technol., Delft, Netherlands
  • fYear
    2013
  • fDate
    21-26 July 2013
  • Firstpage
    884
  • Lastpage
    887
  • Abstract
    Algorithms have been proposed in the recent years in order to retrieve all information available in interferometric stacks of SAR acquisitions with focus on distributed scatterers. One of the key steps in these algorithms - called phase triangulation, phase linking or phase multi-linking - is to optimally estimate filtered wrapped interferometric phases from all possible interferometric combinations preserving useful information and filtering noise. The advantages of these methods compared to conventional approaches are that the algorithm can be applied before phase unwrapping, and that it considers all possible interferograms. In this contribution we propose a new algorithm for phase triangulation based on the integer least squares (ILS) method. We model the phase triangulation problem as a system of linear observation equations. After computing the full covariance matrix of interferometric phases using a Monte-Carlo method, we use ILS to estimate the unknowns. The advantages of our method are that it is capable of considering the mutual correlation between all interferograms, and additionally provides as a output the precision of the estimates. Simulation results show that the proposed method works effectively and can optimally filter noise from interferometric stacks before unwrapping.
  • Keywords
    Monte Carlo methods; covariance matrices; geophysics computing; least squares approximations; radar interferometry; remote sensing by radar; synthetic aperture radar; InSAR stack phase triangulation; Monte-Carlo method; full covariance matrix; integer least squares estimation; interferometric synthetic aperture radar; linear observation equations; phase unwrapping; Covariance matrices; Decorrelation; Mathematical model; Noise; Standards; Time series analysis; Vectors; InSAR; Integer Least Squares estimation; coherence matrix; distributed scatterers (DS);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International
  • Conference_Location
    Melbourne, VIC
  • ISSN
    2153-6996
  • Print_ISBN
    978-1-4799-1114-1
  • Type

    conf

  • DOI
    10.1109/IGARSS.2013.6721301
  • Filename
    6721301