Distributed processing of large remote sensing images using MapReduce - A case of Edge Detection

Detalhes bibliográficos
Autor(a) principal: Tesfamariam, Ermias Beyene
Data de Publicação: 2011
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10362/8279
Resumo: Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.
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spelling Distributed processing of large remote sensing images using MapReduce - A case of Edge DetectionRemote sensingSatellite datasetsComputing modelscomputing technologiesMapReduceLaplacianDissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Advances in sensor technology and their ever increasing repositories of the collected data are revolutionizing the mechanisms remotely sensed data are collected, stored and processed. This exponential growth of data archives and the increasing user’s demand for real-and near-real time remote sensing data products has pressurized remote sensing service providers to deliver the required services. The remote sensing community has recognized the challenge in processing large and complex satellite datasets to derive customized products. To address this high demand in computational resources, several efforts have been made in the past few years towards incorporation of high-performance computing models in remote sensing data collection, management and analysis. This study adds an impetus to these efforts by introducing the recent advancements in distributed computing technologies, MapReduce programming paradigm, to the area of remote sensing. The MapReduce model which is developed by Google Inc. encapsulates the efforts of distributed computing in a highly simplified single library. This simple but powerful programming model can provide us distributed environment without having deep knowledge of parallel programming. This thesis presents a MapReduce based processing of large satellite images a use case scenario of edge detection methods. Deriving from the conceptual massive remote sensing image processing applications, a prototype of edge detection methods was implemented on MapReduce framework using its open-source implementation, the Apache Hadoop environment. The experiences of the implementation of the MapReduce model of Sobel, Laplacian, and Canny edge detection methods are presented. This thesis also presents the results of the evaluation the effect of parallelization using MapReduce on the quality of the output and the execution time performance tests conducted based on various performance metrics. The MapReduce algorithms were executed on a test environment on heterogeneous cluster that supports the Apache Hadoop open-source software. The successful implementation of the MapReduce algorithms on a distributed environment demonstrates that MapReduce has a great potential for scaling large-scale remotely sensed images processing and perform more complex geospatial problems.Foerster, TheodorHenneböhl, KatharinaCaetano, Mário Sílvio Rochinha de AndradeRUNTesfamariam, Ermias Beyene2012-12-03T15:46:21Z2011-02-072011-02-07T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/8279enginfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T17:11:54Zoai:run.unl.pt:10362/8279Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T16:42:58.189496Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Distributed processing of large remote sensing images using MapReduce - A case of Edge Detection
title Distributed processing of large remote sensing images using MapReduce - A case of Edge Detection
spellingShingle Distributed processing of large remote sensing images using MapReduce - A case of Edge Detection
Tesfamariam, Ermias Beyene
Remote sensing
Satellite datasets
Computing models
computing technologies
MapReduce
Laplacian
title_short Distributed processing of large remote sensing images using MapReduce - A case of Edge Detection
title_full Distributed processing of large remote sensing images using MapReduce - A case of Edge Detection
title_fullStr Distributed processing of large remote sensing images using MapReduce - A case of Edge Detection
title_full_unstemmed Distributed processing of large remote sensing images using MapReduce - A case of Edge Detection
title_sort Distributed processing of large remote sensing images using MapReduce - A case of Edge Detection
author Tesfamariam, Ermias Beyene
author_facet Tesfamariam, Ermias Beyene
author_role author
dc.contributor.none.fl_str_mv Foerster, Theodor
Henneböhl, Katharina
Caetano, Mário Sílvio Rochinha de Andrade
RUN
dc.contributor.author.fl_str_mv Tesfamariam, Ermias Beyene
dc.subject.por.fl_str_mv Remote sensing
Satellite datasets
Computing models
computing technologies
MapReduce
Laplacian
topic Remote sensing
Satellite datasets
Computing models
computing technologies
MapReduce
Laplacian
description Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.
publishDate 2011
dc.date.none.fl_str_mv 2011-02-07
2011-02-07T00:00:00Z
2012-12-03T15:46:21Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/8279
url http://hdl.handle.net/10362/8279
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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