Applying time series interferometric synthetic aperture radar and the unscented Kalman filter to predict deformations in Maoxian landslide

Feiyang Xue; Xiaolei Lv

doi:10.1117/1.JRS.13.014509

4 February 2019 Applying time series interferometric synthetic aperture radar and the unscented Kalman filter to predict deformations in Maoxian landslide

Feiyang Xue, Xiaolei Lv

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 13, Issue 1, 014509 (February 2019). https://doi.org/10.1117/1.JRS.13.014509

Abstract

On June 24, 2017, a catastrophic landslide occurred in Maoxian County, Sichuan, China, burying an entire village and more than 100 people. We focused on mapping and predicting the deformation in Maoxian before the landslide based on 39 C-band synthetic aperture radar images acquired by the Sentinel-1 satellite. In order to identify sufficient measurement points in rural areas, the images were first filtered with joint scatterer interferometric synthetic aperture radar to increase the number of measurement points 20 times. Then, the cumulative time series deformation in the landslide area from November 7, 2014, to June 12, 2017, was obtained by employing a time series InSAR technique combined with an unscented Kalman filter (UKF) to predict the deformation by separating the prediction and update steps. Approximately, 30 suspected landslide points were found in the area. Qualitative and quantitative analyses of the experimental results suggested that combining time series InSAR with UKF can be used to predict deformation trends with millimetric accuracy at a large scale before landslides occur.

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Feiyang Xue and Xiaolei Lv "Applying time series interferometric synthetic aperture radar and the unscented Kalman filter to predict deformations in Maoxian landslide," Journal of Applied Remote Sensing 13(1), 014509 (4 February 2019). https://doi.org/10.1117/1.JRS.13.014509

Received: 17 April 2018; Accepted: 10 January 2019; Published: 4 February 2019

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