01897nam a2200193 a 45000010009000000080040000090200028000490200030000770820011001071000032001182450141001502600055002913000020003465201103003666500037014696500027015069990017015339520153015500002501620425s2011    xxu                 eng d  a3845406186 (paperback)   a9783845406183 (paperback)005502221 aTesfamariam, Ermias Beyene.10aDistributed processing of large remote sensing images using mapreduce :b a case of edge detection methods /cErmias Beyene Tesfamariam.  a[S.l.] :bLAP LAMBERT Academic Publishing, c2011.  a84 p. ;c22 cm.  aAdvances 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. 0aEarth sciences xRemote sensing. 0aRemote-sensing images.  c12095d12095  00102ddc4070aBRACULbBRACULcGENd2012-04-25ekarim Internationalg5500.00l2m6o550 TESp3010025016r2022-09-03s2022-08-30t1w2012-04-25yBK