Efficient Ground Surface Displacement Monitoring Using Sentinel-1 Data: Integrating Distributed Scatterers (DS) Identified Using Two-Sample t-Test with Persistent Scatterers (PS)

Abstract

Combining persistent scatterers (PS) and distributed scatterers (DS) is important for effective displacement monitoring using time-series of SAR data. However, for large stacks of synthetic aperture radar (SAR) data, the DS analysis using existing algorithms becomes a time-consuming process. Moreover, the whole procedure of DS selection should be repeated as soon as a new SAR acquisition is made, which is challenging considering the short repeat-observation of missions such as Sentinel-1. SqueeSAR is an approach for extracting signals from DS, which first applies a spatiotemporal filter on images and optimizes DS, then incorporates information from both optimized DS and PS points into interferometric SAR (InSAR) time-series analysis. In this study, we followed SqueeSAR and implemented a new approach for DS analysis using two-sample t-test to efficiently identify neighboring pixels with similar behaviour. We evaluated the performance of our approach on 50 Sentinel-1 images acquired over Trondheim in Norway between January 2015 and December 2016. A cross check on the number of the identified neighboring pixels using the Kolmogorov-Smirnov (KS) test, which is employed in the SqueeSAR approach, and the t-test shows that their results are strongly correlated. However, in comparison to KS-test, the t-test is less computationally intensive (98% faster). Moreover, the results obtained by applying the tests under different SAR stack sizes from 40 to 10 show that the t-test is less sensitive to the number of images.

Elevation change of Bhasan Char measured by persistent scatterer interferometry using Sentinel-1 data in a humanitarian context

“…Furthermore, the preparation interferogram products for the SBAS approach is currently not yet supported in the SNAP software which was used in this study. Yet, to achieve more reliable results with a higher spatial density of PS, future studies of Bhasan Char shout make use of the SBAS approach or consider incorporating distributed scatterers (DS) which are defined as pixels whose neighbouring pixels share similar statistical behaviour (Shamshiri et al, 2018).…”

Surface deformation of Asama volcano, Japan, detected by time series InSAR combining persistent and distributed scatterers, 2014‒2018

“…Figure 7b-d show the mean LOS velocities from the ALOS-2, ascending Sentinel-1, and descending Sentinel-1 images, respectively. Additional file 1: Figure S4 shows the standard deviations of the LOS velocities estimated by the percentile bootstrapping method in StaMPS (Efron and Tibshirani 1986;Shamshiri et al 2018). The reference point (white circle in Fig.…”

Multi-Temporal Small Baseline Interferometric SAR Algorithms: Error Budget and Theoretical Performance

“…More recently, with the scope of maximizing the total number of correctly investigated DS pixels, novel MT-InSAR techniques, based on the processing of long sequences of optimized multi-looked DInSAR interferograms, also emerged, such as SqueeSAR [22], CAESAR [23], and other alternative methods [29,32,42]. Some cross-comparison analyses of the different, developed MT-InSAR techniques [43][44][45][46][47] were also carried out to quantitatively assess their mutual performances. Moreover, in the last seven years, the InSAR community was primarily focused on adapting the available MT-InSAR codes to process large sequences of SAR data collected by the new constellations of SAR satellites.…”