Offline-online change detection for Sentinel-1 InSAR time series
Hussain, Ekbal; Novellino, Alessandro; Jordan, Colm; Bateson, Luke. 2021 Offline-online change detection for Sentinel-1 InSAR time series. Remote Sensing, 13 (9), 1656. https://doi.org/10.3390/rs13091656
Before downloading, please read NORA policies.
|
Text (Open Access Paper)
remotesensing-13-01656-v2.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (2MB) | Preview |
Abstract/Summary
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 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
Item Type: | Publication - Article |
---|---|
Digital Object Identifier (DOI): | https://doi.org/10.3390/rs13091656 |
ISSN: | 2072-4292 |
Date made live: | 06 May 2021 14:10 +0 (UTC) |
URI: | https://nora.nerc.ac.uk/id/eprint/530267 |
Actions (login required)
View Item |
Document Downloads
Downloads for past 30 days
Downloads per month over past year