New advances in intensive DInSAR processing through cloud computing environments

The current Remote Sensing scenario is characterized by the availability of huge archives of SAR data that are going to increase with the advent of Sentinel-1 satellites. The effective exploitation of this large amount of data requires both adequate computing resources as well as advanced algorithms able to properly exploit such facilities. In this work we discuss the migration of the DInSAR technique referred to as Parallel Small BAseline Subset (P-SBAS), which is used for Earth's surface deformation investigation, to the Amazon Web Services (AWS) public Cloud Computing environment. An experimental analysis aimed at evaluating the P-SBAS scalable performances that are achieved within the Cloud environment is presented. The achieved results show very good parallel performances and allow us to identify the major bottlenecks that can hamper such behavior when the amount of data to process highly increases. Accordingly, we present an advanced P-SBAS implementation that is designed to overcome the identified bottlenecks. The experimental analysis is carried out by processing both Envisat and COSMO-SkyMed datasets and by exploiting both a High Performance Computing cluster as well as AWS public Cloud.