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Journal of Emerging Trends in Computing and Information Sciences >> Call for Papers Vol. 8 No. 3, March 2017

Journal of Emerging Trends in Computing and Information Sciences

Cloud Hadoop Map Reduce For Remote Sensing Image Analysis

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Author Mohamed H. Almeer
ISSN 2079-8407
On Pages 637-644
Volume No. 3
Issue No. 4
Issue Date April 01, 2012
Publishing Date April 01, 2012
Keywords Cloud, Hadoop, HPC, Image processing, Map reduce


Abstract

Image processing algorithms related to remote sensing have been tested and utilized on the Hadoop MapReduce parallel platform by using an experimental 112-core high-performance cloud computing system that is situated in the Environmental Studies Center at the University of Qatar. Although there has been considerable research utilizing the Hadoop platform for image processing rather than for its original purpose of text processing, it had never been proved that Hadoop can be successfully utilized for high-volume image files. Hence, the successful utilization of Hadoop for image processing has been researched using eight different practical image processing algorithms. We extend the file approach in Hadoop to regard the whole TIFF image file as a unit by expanding the file format that Hadoop uses. Finally, we apply this to other image formats such as the JPEG, BMP, and GIF formats. Experiments have shown that the method is scalable and efficient in processing multiple large images used mostly for remote sensing applications, and the difference between the single PC runtime and the Hadoop runtime is clearly noticeable.

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