We present a case study on the migration to a cloud computing environment of the 6 advanced differential synthetic aperture radar interferometry (DInSAR) technique, referred to as Small 7 BAseline Subset (SBAS), which is widely used for the investigation of Earth surface deformation 8 phenomena. In particular, we focus on the SBAS parallel algorithmic solution, namely P-SBAS, that 9 allows the production of mean deformation velocity maps and the corresponding displacement time-10 series from a temporal sequence of radar images by exploiting distributed computing architectures. The 11Cloud migration is carried out by encapsulating the overall P-SBAS application in virtual machines 12 running on the cloud; moreover, the cloud resources provisioning and configuration phases are 13 implemented in an automatic way. Such an approach allows us to preserve the P-SBAS parallelization 14 strategy and to straightforwardly evaluate its performance within a cloud environment by comparing it 15 with those achieved on a HPC in-house cluster. The results we present were achieved by using the 16 Amazon Elastic Compute Cloud (EC2) of the Amazon Web Services (AWS) to process SAR datasets 17 collected by the ENVISAT satellite and show that, thanks to the cloud resources availability and 18 flexibility, large DInSAR data volumes can be processed through the P-SBAS algorithm in short time 19 frames and at reduced costs. As a case study, the mean deformation velocity map of the southern 20 California area has been generated by processing 172 ENVISAT images. By exploiting 32 EC2 21 instances this processing took less than 17 hours to complete, with a cost of USD 850. Considering the 22 available PB-scale archives of SAR data and the upcoming huge SAR data flow relevant to the recently 23 launched (April 2014) Sentinel-1A and the forthcoming Sentinel-1B satellites, the exploitation of cloud 24 , IEEE Transactions on Cloud Computing 2 computing solutions is particularly relevant because of the possibility to provide cloud-based multi-user 25 services allowing worldwide scientists to quickly process SAR data and to manage and access the 26 achieved DInSAR results. 27 28 Index Terms-Cloud Computing, DInSAR, P-SBAS, Earth Surface Deformation, Big Data 29 30This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TCC.2015.2440267, IEEE Transactions on Cloud Computing 3 anthropogenic induced land motions) from a sequence of SAR acquisitions. An advanced parallel 49 algorithmic solution for the SBAS approach, referred to as P-SBAS, which implements the complete 50 DInSAR processing chain and is able to exploit distributed computing architectures, has been 51 developed recently [17]. 52 The P-SBAS capability of running on distributed systems, which encompass large, scalable computing 53 resources, is particularly timely considering the current SAR remote sensing scenario that is 54 characterized by the availabi...
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.
The ionosphere is the single largest contributor to the GNSS (Global Navigation Satellite System) error budget and ionospheric scintillation (IS) in particular is one of its most harmful effects. The Ground Based Scintillation Climatology (GBSC) has been recently developed by INGV as a software tool to identify the main areas of the ionosphere in which IS is more likely to occur. Due to the high computational load required, GBSC is currently used only for scientific, offline, studies and not as a real time service. Recently, a collaboration was initiated between ISMB and INGV in order to identify which cloud service model (IaaS, PaaS or SaaS) is most suitable for implementing the GBSC technique within the cloud computing environment. The aims of this joined effort are twofold: i) to optimize the computational resources allocation strategy/plan for the GBSC service, ii) to fine tune the algorithm for dynamic and real time application, towards a service contributing to high precision professional applications for the GNSS-reliant business sectors. Preliminary result of the implementation of GBSC within the cloud environment will be shown.
At the end of September 2009, a new Italian GPS receiver for radio occultation was launched from the Satish Dhawan Space Center (Sriharikota, India) on the Indian Remote Sensing OCEANSAT-2 satellite. The Italian Space Agency has established a set of Italian universities and research centers to implement the overall processing radio occultation chain. After a brief description of the adopted algorithms, which can be used to characterize the temperature, pressure and humidity, the contribution will focus on a method for automatic processing these data, based on the use of a distributed architecture. This paper aims at being a possible application of grid computing for scientific research.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.