Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Offline-Online Change Detection for Sentinel-1 InSAR Time Series

Version 1 : Received: 18 December 2020 / Approved: 21 December 2020 / Online: 21 December 2020 (12:06:44 CET)

A peer-reviewed article of this Preprint also exists.

Hussain, E.; Novellino, A.; Jordan, C.; Bateson, L. Offline-Online Change Detection for Sentinel-1 InSAR Time Series. Remote Sens. 2021, 13, 1656. Hussain, E.; Novellino, A.; Jordan, C.; Bateson, L. Offline-Online Change Detection for Sentinel-1 InSAR Time Series. Remote Sens. 2021, 13, 1656.

Journal reference: Remote Sens. 2021, 13, 1656
DOI: 10.3390/rs13091656

Abstract

Traditional applications of Interferometric Synthetic Aperture Radar (InSAR) data involved inverting an interferogram stack to determine the average displacement velocity. While this approach has useful applications in continuously deforming regions, much information is lost by simply fitting a line through the time series. Thanks to regular acquisitions across most of the the world by the ESA Sentinel-1 satellite constellation, we are now in a position to explore opportunities for near-real time deformation monitoring. In this paper we present a simple statistical approach for detecting offsets and gradient changes in InSAR time series. Our key assumption is that 5 years of Sentinel-1 data is sufficient to calculate the population standard deviation of the detection variables. Our offset detector identifies statistically significant peaks in the first, second and third difference series. The gradient change detector identifies statistically significant movements in the second derivative series. We exploit the high spatial resolution of Sentinel-1 data and the spatial continuity of geophysical deformation signals to filter out false positive detections that arise due to signal noise. When combined with near-real time processing of InSAR data these detectors, particularly the gradient change, could be used to detect incipient ground deformation associated with geophysical phenomena, for example from landslides or volcanic eruptions.

Subject Areas

InSAR; time series; change detection; early warning

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