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001     172735
005     20210129214507.0
024 7 _ |a 10.1109/IGARSS.2014.6946698
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037 _ _ |a FZJ-2014-06179
100 1 _ |a Cavallaro, Gabriele
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111 2 _ |a IGARSS 2014 - 2014 IEEE International Geoscience and Remote Sensing Symposium
|c Quebec City
|d 2014-07-13 - 2014-07-18
|w Canada
245 _ _ |a Smart data analytics methods for remote sensing applications
260 _ _ |c 2014
|b IEEE
295 1 0 |a 2014 IEEE Geoscience and Remote Sensing Symposium
300 _ _ |a 1405 - 1408
336 7 _ |a Contribution to a conference proceedings
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336 7 _ |a Contribution to a book
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336 7 _ |a Conference Paper
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336 7 _ |a CONFERENCE_PAPER
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336 7 _ |a INPROCEEDINGS
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520 _ _ |a The 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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700 1 _ |a Riedel, Morris
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700 1 _ |a Benediktsson, Jon Atli
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700 1 _ |a Goetz, Markus
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700 1 _ |a Runarsson, Tomas
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700 1 _ |a Jonasson, Kristjan
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700 1 _ |a Lippert, Thomas
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773 _ _ |a 10.1109/IGARSS.2014.6946698
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913 2 _ |a DE-HGF
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914 1 _ |y 2014
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