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• W1448378977 abstract "Abstract. Various sensors from airborne and satellite platforms are producing large volumes of remote sensing images for mapping, environmental monitoring, disaster management, military intelligence, and others. However, it is challenging to efficiently storage, query and process such big data due to the data- and computing- intensive issues. In this paper, a Hadoop-based framework is proposed to manage and process the big remote sensing data in a distributed and parallel manner. Especially, remote sensing data can be directly fetched from other data platforms into the Hadoop Distributed File System (HDFS). The Orfeo toolbox, a ready-to-use tool for large image processing, is integrated into MapReduce to provide affluent image processing operations. With the integration of HDFS, Orfeo toolbox and MapReduce, these remote sensing images can be directly processed in parallel in a scalable computing environment. The experiment results show that the proposed framework can efficiently manage and process such big remote sensing data." @default.
• W1448378977 created "2016-06-24" @default.
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• W1448378977 date "2015-07-10" @default.
• W1448378977 modified "2023-10-13" @default.
• W1448378977 title "A HADOOP-BASED DISTRIBUTED FRAMEWORK FOR EFFICIENT MANAGING AND PROCESSING BIG REMOTE SENSING IMAGES" @default.
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• W1448378977 doi "https://doi.org/10.5194/isprsannals-ii-4-w2-63-2015" @default.
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