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dc.contributor.authorKnospe, Steffen H G
dc.contributor.authorJonsson, Sigurjon
dc.date.accessioned2015-08-02T09:14:31Z
dc.date.available2015-08-02T09:14:31Z
dc.date.issued2010-04
dc.identifier.citationKnospe, S., & Jonsson, S. (2010). Covariance Estimation for dInSAR Surface Deformation Measurements in the Presence of Anisotropic Atmospheric Noise. IEEE Transactions on Geoscience and Remote Sensing, 48(4), 2057–2065. doi:10.1109/tgrs.2009.2033937
dc.identifier.issn01962892
dc.identifier.doi10.1109/TGRS.2009.2033937
dc.identifier.urihttp://hdl.handle.net/10754/561573
dc.description.abstractWe study anisotropic spatial autocorrelation in differential synthetic aperture radar interferometric (dInSAR) measurements and its impact on geophysical parameter estimations. The dInSAR phase acquired by the satellite sensor is a superposition of different contributions, and when studying geophysical processes, we are usually only interested in the surface deformation part of the signal. Therefore, to obtain high-quality results, we would like to characterize and/or remove other phase components. A stochastic model has been found to be appropriate to describe atmospheric phase delay in dInSAR images. However, these phase delays are usually modeled as being isotropic, which is a simplification, because InSAR images often show directional atmospheric anomalies. Here, we analyze anisotropic structures and show validation results using both real and simulated data. We calculate experimental semivariograms of the dInSAR phase in several European Remote Sensing satellite-1/2 tandem interferograms. Based on the theory of random functions (RFs), we then fit anisotropic variogram models in the spatial domain, employing Matérn-and Bessel-family correlation functions in nested models to represent complex dInSAR covariance structures. The presented covariance function types, in the statistical framework of stationary RFs, are consistent with tropospheric delay models. We find that by using anisotropic data covariance information to weight dInSAR measurements, we can significantly improve both the precision and accuracy of geophysical parameter estimations. Furthermore, the improvement is dependent on how similar the deformation pattern is to the dominant structure of the anisotropic atmospheric signals. © 2009 IEEE.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectCovariance analysis
dc.subjectCovariance functions
dc.subjectError analysis
dc.subjectGeophysical inverse problems
dc.subjectGeostatistics
dc.subjectRemote Sensing
dc.subjectSynthetic aperture radar (SAR)
dc.titleCovariance estimation for dInSAR surface deformation measurements in the presence of anisotropic atmospheric noise
dc.typeArticle
dc.contributor.departmentCrustal Deformation and InSAR Group
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentEnvironmental Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalIEEE Transactions on Geoscience and Remote Sensing
dc.contributor.institutionInstitute of Geophysics, Swiss Federal Institute of Technology (ETH) Zürich, 8092 Zürich, Switzerland
dc.contributor.institutionInstitute of Geotechnical Engineering and Mine Surveying, Clausthal University of Technology, 38678 Clausthal-Zellerfeld, Germany
dc.contributor.institutionInstitute of Geophysics, ETH Zürich, 8092 Zürich, Switzerland
kaust.personJonsson, Sigurjon


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