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

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## Abstract

**:**

## 1. Introduction

## 2. Methods

#### 2.1. InSAR Processing and Time Series Analysis

#### 2.2. Changes in Displacement Time Series

#### 2.2.1. Offset Detection

#### 2.2.2. Gradient Change Detection

#### 2.2.3. Spatial Filtering

## 3. Results

## 4. Discussion

#### Caveats and Limitations

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Location of the Hatfield Moors test site. The main figure is the land cover classification for the area using Sentinel-2 satellite imagery [22], which shows the study site is mostly peatbog surrounded by agricultural fields. The south and western edges of the site are bounded by lakes and ponds.

**Figure 2.**(

**a**) The average spatial coherence of all initial interferograms represented as a bar centred on the middle of the primary and secondary acquisition dates. The coherence history shows a clear annual cyclicity between a maximum of about 0.8 and a minimum of about 0.4. (

**b**) Baseline-time plot for the interferograms formed in this study. Each line represents an interferogram, colour coded with the average spatial coherence of that image. The dashed red lines are interferograms with average coherence below the threshold value of 0.45 and so were discarded unless they were needed to maintain network connectivity (solid red lines).

**Figure 3.**Two synthetic offset change examples. The top row are displacement time series with offsets at $t=80$ and $t=100$ respectively. There are no gradients before and after the offset in the first example, and different gradients before and after the offset in the second example. There are numerous change detections (shown in red circles) for each of the difference series. But these are reduced when we take the intersection of the 1st, 2nd and 3rd difference detections (bottom row).

**Figure 4.**Stationarity tests on the first and second order difference series. The top row is a synthetic time series with strong seasonal displacements and an offset at $t=100$. We test for stationarity using the Augmented Dickey-Fuller (ADF) test and reject the null hypothesis (the series is not stationary) if the ADF p-value is less than 0.05. We show that the synthetic displacement series is not stationary but the first difference is. The second and third difference fail the stationarity test so we calculate the second order ‘difference of difference’ series for these, after which they pass the stationarity test.

**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.

**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.

**Figure 8.**An example of how the Gaussian blur filter is able to remove isolated pixel detections, which we assume are false positives, leaving the spatially continuous detection clusters. Yellow points are pixels that our algorithm has highlighted as having a change from steady-state (offset or gradient) at this time (28 July 2018). Purple colours are either NaN pixels or pixels with no changes in their displacements. (

**a**) Shows the raw detections using our algorithm, and (

**b**) the results after spatial filtering. (

**c**) are the spatially isolated pixels that were removed by the filter. Axis represent the rows and columns of the pixels

**Figure 9.**Gradient detections using window sizes of 150 days, 100 days, 50 days and 30 days (top to bottom panels respectively). Our algorithm should detect two changes in the displacement gradient between 06-2017 and 09-2017. In all cases we do detect these two changes. However the smaller window size of 30 days is more prone to being affected by noise. For example, the noisy point at 06-2017 indicated on the top panel is detected by the smaller 30 day window, while the larger windows are less sensitive to this.

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**MDPI and ACS Style**

Hussain, E.; Novellino, A.; Jordan, C.; Bateson, L.
Offline-Online Change Detection for Sentinel-1 InSAR Time Series. *Remote Sens.* **2021**, *13*, 1656.
https://doi.org/10.3390/rs13091656

**AMA Style**

Hussain E, Novellino A, Jordan C, Bateson L.
Offline-Online Change Detection for Sentinel-1 InSAR Time Series. *Remote Sensing*. 2021; 13(9):1656.
https://doi.org/10.3390/rs13091656

**Chicago/Turabian Style**

Hussain, Ekbal, Alessandro Novellino, Colm Jordan, and Luke Bateson.
2021. "Offline-Online Change Detection for Sentinel-1 InSAR Time Series" *Remote Sensing* 13, no. 9: 1656.
https://doi.org/10.3390/rs13091656