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.

National Scale Surface Deformation Time Series Generation through Advanced DInSAR Processing of Sentinel-1 Data within a Cloud Computing Environment

“…It is worth underlying that the rationale adopted for the implemented parallelization comes from the studies on the scalability of the P-SBAS approach, which have been thoroughly discussed in [67][68][69]; accordingly, an extensive scalability analysis is here omitted for brevity but can be found in the available literature on this topic [67][68][69]. Moreover, as it is clear from Table IV, the proposed strategy allows us to fully exploit the computing resources (CPUs, RAM and I/O) in the majority of the parallel steps of the processing chain, thus achieving very satisfactory processing times.…”

“…This section is aimed at concisely describing the interferometric processing chain based on the P-SBAS approach [60] developed to process large Sentinel-1 SAR datasets. The P-SBAS algorithm was originally designed to efficiently exploit distributed computing infrastructures for the automatic processing of SAR images acquired through the Stripmap mode, and it has been largely tested with ENVISAT and COSMO-SkyMed SAR datasets [63], [67]- [70]. Here, we present the main modifications applied to the P-SBAS Stripmap processing chain in order to handle S1 IWS SAR data, which are collected through the TOPS acquisition mode [58] and, therefore, are composed by several bursts, which can be treated as independent portions of data.…”

“…In this scenario, we already developed some works regarding the migration of the P-SBAS processing chain to the Amazon Web Services (AWS) CC environment [65], [66]. These works concerned the evaluation of the P-SBAS parallel performances achievable within a CC infrastructure and the subsequent identification of the main issues affecting the P-SBAS scalability, i.e., the capability to efficiently use increasing resources in order to sustain the processing of Big Data volumes and to reduce the computing elapsed times [67]- [69].…”

Foreword to the Special Issue on Big Data in Remote Sensing

“…A scalable Web service for near real-time land cover mapping is provided in a cloud computing (CC) environment in terms of high-resolution remote sensing images [9]. Also, processing deformation maps and time series of differential synthetic aperture radar interferometry (DInSAR) is carried out in particular in an Amazon Web Services (AWS) CC platform [10]. Furthermore, a visualization strategy is utilized to label SAR images based on an active learning algorithm [11].…”

A Fast Synthetic Aperture Radar Raw Data Simulation Using Cloud Computing

“…As the outstanding capacity of the cloud computing framework, the MapReduce implementations of classical algorithms have drawn increasing attention. Cloud computing has been applied to remote sensing processing [ 29 ], geoscience [ 30 ], SAR interferometry [ 31 ], image processing [ 22 ] and other remote sensing areas. Cloud computing is the future trend of the remote sensing big data processing.…”