A First Assessment of the P-SBAS DInSAR Algorithm Performances Within a Cloud Computing Environment

Abstract

We present in this work a first performance assessment of the Parallel Small BAseline Subset (P-SBAS) algorithm, for the generation of Differential Synthetic Aperture Radar (SAR) Interferometry (DInSAR) deformation maps and time series, which has been migrated to a Cloud Computing (CC) environment. In particular, we investigate the scalable performances of the P-SBAS algorithm by processing a selected ENVISAT ASAR image time series, which we use as a benchmark, and by exploiting the Amazon Web Services (AWS) CC platform. The presented analysis shows a very good match between the theoretical and experimental P-SBAS performances achieved within the CC environment. Moreover, the obtained results demonstrate that the implemented P-SBAS Cloud migration is able to process ENVISAT SAR image time series in short times (less than 7 h) and at low costs (about USD 200). The P-SBAS Cloud scalable performances are also compared to those achieved by exploiting an in-house High Performance Computing (HPC) cluster, showing that nearly no overhead is introduced by the presented Cloud solution. As a further outcome, the performed analysis allows us to identify the major bottlenecks that can hamper the P-SBAS performances within a CC environment, in the perspective of processing very huge SAR data flows such as those coming from the existing COSMO-SkyMed or the upcoming SENTINEL-1 constellation. This work represents a relevant step toward the challenging Earth Observation scenario focused on the joint exploitation of advanced DInSAR techniques and CC environments for the massive processing of Big SAR Data.

Keywords

• Big data
• Cloud Computing (CC)
• Differential Synthetic Aperture Radar (SAR) Interferometry (DInSAR)
• Earth surface deformation
• Parallel Small BAseline Subset (P-SBAS)