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

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 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.

Landslide detection, monitoring and prediction with remote-sensing techniques

“…Concerning satellite SAR, displacement time series are the most advanced product of any multitemporal interferometric processing and provide the temporal pattern of deformation of each MP for each acquisition over the observed period. When supported by other instrumentation and field surveys, visual 114 , semi-automatic 115 and automatic analysis 116 of time series can support the characterization and monitoring of landslide kinematics 117 , zonation of sectors 118 , the identification of post-seismic 119 and rainfall-induced 120 velocity changes, seasonal variations 121 and the monitoring of remedial works performance 122 . Furthermore, the shorter than a week revisiting time of satellites such as Sentinel-1, TerraSAR-X or COSMO-SkyMed makes landslide monitoring at the regional scale feasible with a systematic regularity 123 .…”

Monitoring CO2 Storage Sites Onshore and Offshore using InSAR Data and Strain Sensing Fibre Optics Cables

“…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. The results of this work were published in [5]. Electronic copy available at: https://ssrn.com/abstract=4286039…”

A Clustering Approach for the Analysis of InSAR Time Series: Application to the Bandung Basin (Indonesia)

“…These changes have led to an increase in the amount of information that can be extracted from these datasets and have resulted in a more comprehensive understanding of the evolution of the Earth's surface and subsurface processes. However, until a few years ago InSAR interpretation was mainly limited to analysis of the average displacement rates [19], but advances in innovative big data analysis methods change this and enable exploitation of the full displacement time series [20][21][22] In the case of large InSAR datasets, traditional manual analysis is a complex and timeconsuming process and more automated techniques are necessary. Previous work has used a variety of methods to help interpret InSAR time series, ranging from semi-automatic [21,23,24] and automatic statistical approaches [25], to the use of supervised [26,27] and unsupervised [20,28,29] machine learning algorithms.…”