Emi: Efficient temporal phase estimation and its impact on high-precision insar time series analysis

Homa Ansari, Francesco De Zan, Giorgio Gomba, Richard Bamler

Research output: Contribution to conferencePaperpeer-review

9 Scopus citations

Abstract

Multitemporal phase estimation aims at the exploitation temporal data redundancy within the SAR time-series to reduce the impact of inherent stochastic and systematic interferometric phase inconsistencies [1] for distributed scatterers (DS). The consistent phase-series estimated as such is further utilized to retrieve the underlying geophysical and atmospheric signals. Therefore, the precision and interpretability of the retrieved physical signals from the DS is governed by the performance of the phase estimators. Different approaches to phase estimation calls for the investigation of their performance. Here we explain the discrepancy among the different approaches in terms of their underlying covariance model and introduce our recently proposed estimator named EMI [2]. Bridging between different approaches via revised mathematical formulation of phase estimation, EMI enhances the estimation precision and computational efficiency of the temporal phase estimation. The performance of different phase estimators is brought into attention via simulation analysis. Using Sentinel-1 time series over the North and East Anatolian Faults, wide area performance analysis is further carried out and will be presented.

Original languageEnglish
Pages270-273
Number of pages4
DOIs
StatePublished - 2019
Event39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan
Duration: 28 Jul 20192 Aug 2019

Conference

Conference39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Country/TerritoryJapan
CityYokohama
Period28/07/192/08/19

Keywords

  • Estimation efficiency
  • Interferometric synthetic aperture radar
  • Low-rank approximation
  • Maximum likelihood estimation
  • Sample covariance matrix

Fingerprint

Dive into the research topics of 'Emi: Efficient temporal phase estimation and its impact on high-precision insar time series analysis'. Together they form a unique fingerprint.

Cite this