Distributed Processing Large Remote by Tesfamariam Ermias (6 results)

Taschenbuch. Condition: Neu. Distributed Processing of Large Remote Sensing Images Using MapReduce | A Case Of Edge Detection Methods | Ermias Beyene Tesfamariam | Taschenbuch | 84 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783845406183 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Advances in remote sensing technology and their ever increasing repositories of the collected data are revolutionizing the mechanisms these data are collected, stored and processed. This exponential growth of data archives and the increasing users demand for real-and near-real time remote sensing data products has challenged the data 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 and several efforts have been made in the past few years towards incorporation of high-performance computing models. This study analyzes the recent advancements in distributed computing technologies, the MapReduce programming model, extends it for use in the area of remote sensing image processing. Performance tests for processing of large archives of Landsat images were performed with the Hadoop framework. The findings demonstrate that MapReduce has a potential for scaling large-scale remotely sensed images processing and perform more complex geospatial problems. 84 pp. Englisch.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Tesfamariam Ermias BeyeneI am a Geospatial Information Specialist and I hold a Master of Science in Geospatial Technologies. My research interests are remote sensing, Spatio-temporal analysis, and geostatistcs for earth and environme.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Advances in remote sensing technology and their ever increasing repositories of the collected data are revolutionizing the mechanisms these data are collected, stored and processed. This exponential growth of data archives and the increasing users' demand for real-and near-real time remote sensing data products has challenged the data 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 and several efforts have been made in the past few years towards incorporation of high-performance computing models. This study analyzes the recent advancements in distributed computing technologies, the MapReduce programming model, extends it for use in the area of remote sensing image processing. Performance tests for processing of large archives of Landsat images were performed with the Hadoop framework. The findings demonstrate that MapReduce has a potential for scaling large-scale remotely sensed images processing and perform more complex geospatial problems.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 84 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Advances in remote sensing technology and their ever increasing repositories of the collected data are revolutionizing the mechanisms these data are collected, stored and processed. This exponential growth of data archives and the increasing users demand for real-and near-real time remote sensing data products has challenged the data 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 and several efforts have been made in the past few years towards incorporation of high-performance computing models. This study analyzes the recent advancements in distributed computing technologies, the MapReduce programming model, extends it for use in the area of remote sensing image processing. Performance tests for processing of large archives of Landsat images were performed with the Hadoop framework. The findings demonstrate that MapReduce has a potential for scaling large-scale remotely sensed images processing and perform more complex geospatial problems.