Standard

Time-Series Clustering Methodology for Estimating Atmospheric Phase Screen in Ground-Based InSAR Data. / Izumi, Yuta; Nico, Giovanni; Sato, Motoyuki.

In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 60, 01.01.2022.

Research output: Contribution to journal › Article › peer-review

Harvard

Izumi, Y, Nico, G & Sato, M 2022, 'Time-Series Clustering Methodology for Estimating Atmospheric Phase Screen in Ground-Based InSAR Data', IEEE Transactions on Geoscience and Remote Sensing, vol. 60. https://doi.org/10.1109/TGRS.2021.3072037

APA

Izumi, Y., Nico, G., & Sato, M. (2022). Time-Series Clustering Methodology for Estimating Atmospheric Phase Screen in Ground-Based InSAR Data. IEEE Transactions on Geoscience and Remote Sensing, 60. https://doi.org/10.1109/TGRS.2021.3072037

Vancouver

Izumi Y, Nico G, Sato M. Time-Series Clustering Methodology for Estimating Atmospheric Phase Screen in Ground-Based InSAR Data. IEEE Transactions on Geoscience and Remote Sensing. 2022 Jan 1;60. https://doi.org/10.1109/TGRS.2021.3072037

Author

Izumi, Yuta ; Nico, Giovanni ; Sato, Motoyuki. / Time-Series Clustering Methodology for Estimating Atmospheric Phase Screen in Ground-Based InSAR Data. In: IEEE Transactions on Geoscience and Remote Sensing. 2022 ; Vol. 60.

BibTeX

@article{e99f8a476637426db6e4d192bf4adae7,
title = "Time-Series Clustering Methodology for Estimating Atmospheric Phase Screen in Ground-Based InSAR Data",
abstract = "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.",
keywords = "Atmospheric phase screen (APS), differential radar interferometry, ground-based synthetic aperture radar (GB-SAR), interferometric SAR (InSAR), k-means clustering, time-series InSAR",
author = "Yuta Izumi and Giovanni Nico and Motoyuki Sato",
year = "2022",
month = jan,
day = "1",
doi = "10.1109/TGRS.2021.3072037",
language = "English",
volume = "60",
journal = "IEEE Transactions on Geoscience and Remote Sensing",
issn = "0196-2892",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

RIS

TY - JOUR

T1 - Time-Series Clustering Methodology for Estimating Atmospheric Phase Screen in Ground-Based InSAR Data

AU - Izumi, Yuta

AU - Nico, Giovanni

AU - Sato, Motoyuki

PY - 2022/1/1

Y1 - 2022/1/1

N2 - 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.

AB - 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.

KW - Atmospheric phase screen (APS)

KW - differential radar interferometry

KW - ground-based synthetic aperture radar (GB-SAR)

KW - interferometric SAR (InSAR)

KW - k-means clustering

KW - time-series InSAR

UR - http://www.scopus.com/inward/record.url?scp=85105053191&partnerID=8YFLogxK

U2 - 10.1109/TGRS.2021.3072037

DO - 10.1109/TGRS.2021.3072037

M3 - Article

AN - SCOPUS:85105053191

VL - 60

JO - IEEE Transactions on Geoscience and Remote Sensing

JF - IEEE Transactions on Geoscience and Remote Sensing

SN - 0196-2892

ER -

ID: 114329425