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000172735 0247_ $$2doi$$a10.1109/IGARSS.2014.6946698
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000172735 037__ $$aFZJ-2014-06179
000172735 1001_ $$0P:(DE-HGF)0$$aCavallaro, Gabriele$$b0$$eCorresponding Author
000172735 1112_ $$aIGARSS 2014 - 2014 IEEE International Geoscience and Remote Sensing Symposium$$cQuebec City$$d2014-07-13 - 2014-07-18$$wCanada
000172735 245__ $$aSmart data analytics methods for remote sensing applications
000172735 260__ $$bIEEE$$c2014
000172735 29510 $$a2014 IEEE Geoscience and Remote Sensing Symposium
000172735 300__ $$a1405 - 1408
000172735 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1420633340_23894
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000172735 3367_ $$033$$2EndNote$$aConference Paper
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000172735 3367_ $$2BibTeX$$aINPROCEEDINGS
000172735 520__ $$aThe big data analytics approach emerged that can be interpreted as extracting information from large quantities of scientific data in a systematic way. In order to have a more concrete understanding of this term we refer to its refinement as smart data analytics in order to examine large quantities of scientific data to uncover hidden patterns, unknown correlations, or to extract information in cases where there is no exact formula (e.g. known physical laws). Our concrete big data problem is the classification of classes of land cover types in image-based datasets that have been created using remote sensing technologies, because the resolution can be high (i.e. large volumes) and there are various types such as panchromatic or different used bands like red, green, blue, and nearly infrared (i.e. large variety). We investigate various smart data analytics methods that take advantage of machine learning algorithms (i.e. support vector machines) and state-of-the-art parallelization approaches in order to overcome limitations of big data processing using non-scalable serial approaches.
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000172735 7001_ $$0P:(DE-Juel1)132239$$aRiedel, Morris$$b1$$ufzj
000172735 7001_ $$0P:(DE-HGF)0$$aBenediktsson, Jon Atli$$b2
000172735 7001_ $$0P:(DE-Juel1)162390$$aGoetz, Markus$$b3$$ufzj
000172735 7001_ $$0P:(DE-HGF)0$$aRunarsson, Tomas$$b4
000172735 7001_ $$0P:(DE-HGF)0$$aJonasson, Kristjan$$b5
000172735 7001_ $$0P:(DE-Juel1)132179$$aLippert, Thomas$$b6$$ufzj
000172735 773__ $$a10.1109/IGARSS.2014.6946698
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000172735 9132_ $$0G:(DE-HGF)POF3-512$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data $$vData-Intensive Science and Federated Computing$$x0
000172735 9131_ $$0G:(DE-HGF)POF2-412$$1G:(DE-HGF)POF2-410$$2G:(DE-HGF)POF2-400$$3G:(DE-HGF)POF2$$4G:(DE-HGF)POF$$aDE-HGF$$bSchlüsseltechnologien$$lSupercomputing$$vGrid Technologies and Infrastructures$$x0
000172735 9141_ $$y2014
000172735 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
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