Sentinel-L Global Coverage Foreshortening Mask Extraction: an Open Source Implementation Based on Google Earth Engine

Mohammad Kakooei, Andrea Nascetti, Yifang Ban

Abstract: It is well known that SAR imagery is affected by SAR geometric distortions due to the SAR imaging process (i.e. Layover, Foreshortening and Shadows). Specially in mountainous areas these distortions affect large portions of the images and in some applications, these areas shouldn't be included in analysis. Using a foreshortening mask is a suitable solution, but finding the mask is challenging. The aim of this research is to exploit the fusion of Sentinel-1 multi-temporal images and SRTM DEM to produce a quasi-global foreshortening mask using the Google Earth Engine (GEE), cloud-based platform. The mean value of multi-temporal Sentinel-1 images is calculated. Then a local minimum algorithm finds probable foreshortening area. Aspect and slope information from SRTM DEM are used to refine Sentinel-1 derived foreshortening mask. The proposed method is tested in British Columbia (Canada), Everest Mountain (Nepal), and Mazandaran (Iran). The results demonstrate the reliability of proposed method to detect the foreshortening area.