Ivana Zinno, Francesco Casu, Claudio De Luca, Riccardo Lanari, Michele Manunta. CNR IREA, Napoli, Italy
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1 An Unsupervised Implementation of the P-SBAS DiNSAR Algorithm for Processing Large Data Volumes through Distributed Computing Infrastructures within Operational Environments Ivana Zinno, Francesco Casu, Claudio De Luca, Riccardo Lanari, Michele Manunta CNR IREA, Napoli, Italy
2 Outline DInSAR Background Motivations driving towards Cloud Platforms exploitation P-SBAS Cloud Deployment within the Amazon Web Services (AWS) and relevant results P-SBAS web tool within the ESA Geohazard Exploitation Platform (GEP) and relevant results
3 DInSAR scenario Background S1
4 DInSAR scenario Background S1 (t 1 ) Baseline S2 (t 2 )
5 Background DInSAR scenario Interferogram 3/2000-7/2000 S1 (t1) Napoli Bay, Campi Flegrei Baseline S2 (t2) π λ/2 -π
6 Interferograms Background Advanced DInSAR: the SBAS algorithm Exploiting the existing SAR huge data archives, we can move from the analysis of single deformation events to the generation of time-series. <-1 Deformation Velocity [cm/y] <-1.5 >1.5 Napoli bay area ( )
7 Motivations SBAS Application Scenario Earthquakes Volcanoes SAR data set SBAS processing Water Resources
8 Motivations Past, present and future SAR Satellite Constellations swath width: 40 km revisit time: 4-11 days swath width: 250 km revisit time: 12-6 days Sentinel swath width: 100 km revisit time: monthly Time
9 Motivations Cloud platforms exploitation huge amount of data Infinite processing computing can be a resources at affordable bottleneck costs a lot of applications
10
11 P-SBAS Cloud Deployment Parallel SBAS (P-SBAS) workflow Casu et al., 2014, SBAS-DInSAR Parallel Processing for Deformation Time-Series Computation, IEEE JSTARS Zinno et al., 2015, A First Assessment of the P-SBAS DInSAR Algorithm Performances Within a Cloud Computing Environment, IEEE JSTARS Zinno et al., 2015, Cloud Computing for Earth Surface Deformation Analysis - Parallel via Steps Spaceborne Radar Imaging: A Case Study, IEEE Transaction - Sequential Steps Cloud Computing DUAL LEVEL PARALLELISM FOR MULTI-NODE AND MULTI-CORE ARCHITECTURES
12 P-SBAS Cloud Deployment Computing architecture implemented within AWS NFS-based distributed storage implementation designed to minimize the I/O and data transfer overhead
13 P-SBAS results on AWS: the Southern California DInSAR analysis P-SBAS Cloud Deployment: Results n. ENVISAT frames 18 (Southern California) n. SAR images 741 Coverage 150,000 km 2 n. AWS Instances 144 n. CPUs 1,152 Input storage Overall storage 550 GB 11 TB
14 P-SBAS Cloud Deployment: Results P-SBAS ENVISAT results on Amazon AWS Cloud Overall Processing time Total cost Average cost per ENVISAT frame * GPS SAR < 9 hours < 1000 USD ~ 55 USD Los Angeles >1.5 Deformation velocity [cm/yr] San Diego <-1.5
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16 P-SBAS web tool within GEP ESA Geohazard Exploitation Platform (GEP) EO data storage: ERS, ENVISAT, Sentinel (SciHub) Processing tools: SBAS, ROI_PAC, StaMPS, Computation: Cloud and GRID (G-POD)
17 P-SBAS web tool within GEP ESA Geohazard Exploitation Platform
18 GEP P-SBAS service P-SBAS web tool within GEP Provided as Web Processing Service (WPS) via Grid Processing On Demand (G-POD) of ESA
19 P-SBAS Service Usage Multiple Runs Single Run P-SBAS web tool within GEP ~ 50 Interferometric Analyses between January-May 2015 # WN # Images # Interf # Coherent Points Elapsed Time days Average P-SBAS service processing performance
20 P-SBAS results within GEP: the Napoli bay area <-1 cm/yr >1 P-SBAS web tool within GEP: Results Dataset: 64 ENVISAT acquisitions Time span: * GPS SAR
21 P-SBAS web tool within GEP: Results Sentinel-1: towards global coverage Napoli Bay
22 P-SBAS web tool within GEP: Results Sentinel-1: monthly coverage
23 P-SBAS web tool within GEP: Results Sentinel-1 on G-POD: Inteferograms Generation
24 P-SBAS web tool within GEP: Results Sentinel-1 on G-POD: Inteferograms Generation
25 P-SBAS web tool within GEP: Results Sentinel-1A 12-days Coherence over Europe Number of Images (slices): 300 (150 interferometric pairs) Time Period: June-July 2015 Processed Area : 7,500,000 km 2 Covered Area: 3,200,000 km 2 This work has been carried out within the ESA G-POD environment. 0 Coherence 1
26 Ask for an Account to: GEP:
27 Remote Sensing of Environment ELSEVIER Special Issue on Big Remotely Sensed Data: tools, applications and experiences Submissions Due July 1, 2016
28
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