Home > Publications database > Scalable developments for big data analytics in remote sensing > print

001     276338
005     20210129220845.0
024 7 _ |2 doi
|a 10.1109/IGARSS.2015.7326030
037 _ _ |a FZJ-2015-06798
041 _ _ |a English
100 1 _ |0 P:(DE-HGF)0
|a Cavallaro, G.
|b 0
|e Corresponding author
111 2 _ |a IEEE International Geoscience and Remote Sensing Symposium
|g IGARSS 2015
|c Milan
|d 2015-07-26 - 2015-07-31
|w Italy
245 _ _ |a Scalable developments for big data analytics in remote sensing
260 _ _ |b IEEE
|c 2015
295 1 0 |a 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) : [Proceedings] - IEEE, 2015. - ISBN 978-1-4799-7929-5
300 _ _ |a 1366 - 1369
336 7 _ |a Contribution to a conference proceedings
|b contrib
|m contrib
|0 PUB:(DE-HGF)8
|s 1448465227_32104
|2 PUB:(DE-HGF)
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a CONFERENCE_PAPER
|2 ORCID
336 7 _ |a Output Types/Conference Paper
|2 DataCite
336 7 _ |a conferenceObject
|2 DRIVER
336 7 _ |a INPROCEEDINGS
|2 BibTeX
520 _ _ |a Big Data Analytics methods take advantage of techniques from the fields of data mining, machine learning, or statistics with a focus on analysing large quantities of data (aka ‘big datasets’) with modern technologies. Big data sets appear in remote sensing in the sense of large volumes, but also in the sense of an ever increasing amount of spectral bands (i.e., high-dimensional data). The remote sensing has traditionally used the above described techniques for a wide variety of application such as classification (e.g., land cover analysis using different spectral bands from satellite data), but more recently scalability challenges occur when using traditional (often serial) methods. This paper addresses observed scalability limits when using support vector machines (SVMs) for classification and discusses scalable and parallel developments used in concrete application areas of remote sensing. Different approaches that are based on massively parallel methods are discussed as well as recent developments in parallel methods.
536 _ _ |0 G:(DE-HGF)POF3-512
|a 512 - Data-Intensive Science and Federated Computing (POF3-512)
|c POF3-512
|f POF III
|x 0
588 _ _ |a Dataset connected to CrossRef Conference
700 1 _ |0 P:(DE-Juel1)132239
|a Riedel, Morris
|b 1
|u fzj
700 1 _ |0 P:(DE-Juel1)164357
|a Bodenstein, C.
|b 2
|u fzj
700 1 _ |0 P:(DE-Juel1)8832
|a Glock, P.
|b 3
|u fzj
700 1 _ |0 P:(DE-Juel1)145217
|a Richerzhagen, M.
|b 4
|u fzj
700 1 _ |0 P:(DE-Juel1)162390
|a Goetz, M.
|b 5
|u fzj
700 1 _ |0 P:(DE-HGF)0
|a Benediktsson, J. A.
|b 6
773 _ _ |a 10.1109/IGARSS.2015.7326030
909 C O |o oai:juser.fz-juelich.de:276338
|p VDB
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)132239
|a Forschungszentrum Jülich GmbH
|b 1
|k FZJ
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)164357
|a Forschungszentrum Jülich GmbH
|b 2
|k FZJ
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)8832
|a Forschungszentrum Jülich GmbH
|b 3
|k FZJ
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)145217
|a Forschungszentrum Jülich GmbH
|b 4
|k FZJ
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)162390
|a Forschungszentrum Jülich GmbH
|b 5
|k FZJ
913 1 _ |0 G:(DE-HGF)POF3-512
|1 G:(DE-HGF)POF3-510
|2 G:(DE-HGF)POF3-500
|a DE-HGF
|b Key Technologies
|v Data-Intensive Science and Federated Computing
|x 0
|l Supercomputing & Big Data
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
914 1 _ |y 2015
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
|x 0
980 _ _ |a contrib
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)JSC-20090406
980 _ _ |a UNRESTRICTED

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21