De Luca, Claudio (2016) An insight in cloud computing solutions for intensive processing of remote sensing data. [Tesi di dottorato]

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Item Type: Tesi di dottorato
Lingua: English
Title: An insight in cloud computing solutions for intensive processing of remote sensing data
Creators:
CreatorsEmail
De Luca, Claudiodeluca.c@irea.cnr.it
Date: 31 March 2016
Number of Pages: 181
Institution: Università degli Studi di Napoli Federico II
Department: Ingegneria Elettrica e delle Tecnologie dell'Informazione
Scuola di dottorato: Ingegneria dell'informazione
Dottorato: Ingegneria informatica ed automatica
Ciclo di dottorato: 28
Coordinatore del Corso di dottorato:
nomeemail
Garofalo, Francofranco.garofalo@unina.it
Tutor:
nomeemail
Casola, ValentinaUNSPECIFIED
Date: 31 March 2016
Number of Pages: 181
Uncontrolled Keywords: Cloud Computing; P-SBAS; G-POD; AWS; DInSAR
Settori scientifico-disciplinari del MIUR: Area 01 - Scienze matematiche e informatiche > INF/01 - Informatica
Area 09 - Ingegneria industriale e dell'informazione > ING-INF/03 - Telecomunicazioni
Date Deposited: 03 May 2016 13:03
Last Modified: 16 Nov 2016 12:38
URI: http://www.fedoa.unina.it/id/eprint/11037

Abstract

The investigation of Earth's surface deformation phenomena provides critical insights into several processes of great interest for science and society, especially from the perspective of further understanding the Earth System and the impact of the human activities. Indeed, the study of ground deformation 
phenomena can be helpful for the comprehension of the geophysical dynamics dominating natural 
hazards such as earthquakes, volcanoes and landslide. In this context, the microwave space-borne Earth Observation (EO) techniques represent very powerful instruments for the ground deformation estimation. In particular, Small BAseline Subset (SBAS) is regarded as one of the key techniques, for its ability to investigate surface deformation affecting large areas of the Earth with a centimeter to millimeter accuracy in different scenarios (volcanoes, tectonics, landslides, anthropogenic induced land motions). The current Remote Sensing scenario is characterized by the availability of huge archives of radar 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 concentrated on the use of the P-SBAS algorithm (a parallel version of SBAS) within HPC infrastructure, to finally investigate the effectiveness of such technologies for EO applications. In particular we demonstrated that the cloud computing solutions represent a valid alternative for scientific application and a promising research scenario, indeed, from all the experiments that we have conducted and from the results obtained performing Parallel Small Baseline Subset (P-SBAS) processing, the cloud technologies and features result to be absolutely competitive in terms of performance with in-house HPC cluster solution.

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