EVALUATION OF PIXEL SELECTION METHODS FOR TRAFFIC INFRASTRUCTURE MONITORING USING SENTINEL-1 INSAR

Abstract

Abstract. The Synthetic Aperture Radar (SAR) satellite Sentinel-1 provides excellent traffic infrastructure monitoring capabilities due to its short revisit time, wide-scale coverage and free of charge data policy. However, pixels from Sentinel-1 have a medium spatial resolution so that traffic infrastructure is only covered by a few pixels. Moreover, Interferometric Synthetic Aperture Radar (InSAR) yields deformation time series for coherent pixels only, thus limiting the number of pixels reporting the ground motion at traffic infrastructure. Although various InSAR time series methods have been successfully applied for traffic infrastructure monitoring, the selection of appropriate methods achieving a high pixel density has not yet been evaluated. In this study, we test whether we can improve the monitoring capabilities by combining different InSAR time series methods. On the one hand, we applied widely used Stanford Method for Persistent Scatterers (StaMPS) as a baseline method to retrieve the deformation time series. On the other hand, we enhanced the phase quality in a pre-processing step using the Phase-Linking (PL) approach and afterwards estimated the deformation time series also with StaMPS based on pixels selected by PL. We compared and evaluated the achieved pixel densities from both methods at main roads, highways and railways in a study area in Germany. We found that the InSAR time series methods selected complementary sets of pixels at all traffic infrastructure types. Moreover, the results indicate a great potential for railway monitoring using Sentinel-1 InSAR due to both high pixel density and homogeneous spatial distribution of pixels. Interestingly, coherent scatterers selected by StaMPS were observed to coincide with large traffic signs at highways which show a double-bounce scattering in the amplitude images. This study shows the benefits of combining PL and StaMPS for increased pixel density at traffic infrastructure and confirms Sentinel-1 data as a suitable data source for traffic infrastructure monitoring.