# Offline-Online Change Detection for Sentinel-1 InSAR Time Series
## 3 Results  
We apply and test our method on an InSAR stack processed over Hatfield Moors in the UK (Figure 1). Since 2000 Scottish Power have been using the natural underground sandstone reservoir beneath the site as a gas storage facility. The superficial land cover of the site consists mostly of peatland surrounded by agricultural fields. The south and western edges of the site are bounded by lakes and ponds.  
Of the 509 Sentinel-1 interferograms processed over the Hatfield Moors test site, we discarded 85 due to poor coherence (Figure 2b). We inverted the remaining 424 interferograms to form a displacement time series over the region spanning 257 dates between 28 March 2015 and 18 May 2020. The average line-of-sight velocity through the time series is shown in (Figure 6), where negative blue colours represent increase in line-of-sight displacement rate from the satellite. While most pixels have an average velocity between \(\pm\)5 mm/yr, there are a few pixels to the east with range increase up to 15 mm/yr.  
We apply our change detection algorithm to determine offsets and gradient changes for each of the pixel time series in this image. For the gradient change detection we chose an arbitrary window size of 50 days and a smoothing window of 15 days. A random selection of the results covering the study area are shown in Figure 7, where the black circles are detected offsets and blue bars represent the window in which we detect a gradient change. Due to the small region of study, the time series show similar deformation patterns. In general, upon visual inspection we are able to pick out the main turning points within each series, particularly the steps in displacement.  
Figure 5: Two synthetic gradient change examples. The orange line in the top figure is the 15-day smoothed series. Example one uses a detection window of 20 days while we use 40 days for example two.  
We remove isolated pixel detections, which we assume are false positives, using the method described in Section 2.2.3. We apply the Gaussain blur filter to each 2D detection map in the time series using a kernel window of approximately 200 m. On average, spatial filtering resulted in a 26% reduction in the number of detections across the entire stack (Figure 8).  
While the aim of this paper is not to interpret the physical cause behind these patterns, we suspect that the steps in the time series are related to a combination of groundwater  
Figure 6: The average line-of-sight velocity for the study region. The white star at (lon lat: \(-0.966\), \(53.544\)) is the reference pixel. Blue colours represent distance increase from the satellite. Clear areas indicate where no measurements were possible due to poor coherence. The blue squares are the locations of the pixel time series shown in Figure 7. Points \(a\), \(c\), \(d\), \(e\) and \(h\) correspond to the Peatbogs land cover class in Figure 1, point \(b\) is on Herbaceous vegetation and point \(g\) on Cultivated area.  
Figure 7: Selected pixel time series covering the study region with offset and gradient changes detected using our algorithm. The location of each pixel is indicated in Figure 6. We chose a window size of 50 days for the gradient change detection. With our detection method we are able to pick out most of the turning points within each time series.  
level changes within the peat, which is known to follow annual precipitation (e.g., [38; 39]), and periodic gas injection and withdrawal from the sediments in this region. Disentangling these signals will require further investigation and field studies. These are ongoing as part of the SENSE research project ([[https://sense-act.eu](https://sense-act.eu)]([https://sense-act.eu](https://sense-act.eu))).