Time-Series Clustering Methodology for Estimating Atmospheric Phase Screen in Ground-Based InSAR Data

Yuta Izumi, Giovanni Nico, Motoyuki Sato

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


In multitemporal interferometric synthetic aperture radar (InSAR) applications, propagation delay in the troposphere introduces a major source of disturbance known as atmospheric phase screen (APS). This study proposes a novel framework to compensate for the APS from multitemporal ground-based InSAR data. The proposed framework first performs time-series clustering in accordance with the temporal APS behavior realized by the $k$ -means clustering approach. In the second step, joint estimation of the APS and displacement velocity is performed. For this purpose, a novel interferometric signal model, including the APS modeled by the median profiles defined in each cluster, is proposed. The proposed framework is validated with the Ku-band ground-based synthetic aperture radar data sets measured over a mountainous area in Kumamoto, Japan. Tests on these data sets reveal that compared with the conventional approach, the presented approach improves displacement estimation accuracy under severe atmospheric conditions.

Original languageEnglish
JournalIEEE Transactions on Geoscience and Remote Sensing
Publication statusPublished - 2022


  • Atmospheric phase screen (APS)
  • differential radar interferometry
  • ground-based synthetic aperture radar (GB-SAR)
  • interferometric SAR (InSAR)
  • k-means clustering
  • time-series InSAR

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)


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