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

Yuta Izumi, Giovanni Nico, Motoyuki Sato

研究成果: Article査読

1 被引用数 (Scopus)

抄録

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.

本文言語English
ジャーナルIEEE Transactions on Geoscience and Remote Sensing
60
DOI
出版ステータスPublished - 2022

ASJC Scopus subject areas

  • 電子工学および電気工学
  • 地球惑星科学(全般)

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