Home > Publications database > Multi-Scale Convolutional SVM Networks for Multi-Class Classification Problems of Remote Sensing Images > print

001     867900
005     20230918092306.0
024 7 _ |a 10.1109/IGARSS.2019.8899831
|2 doi
024 7 _ |a 2128/23600
|2 Handle
024 7 _ |a WOS:000519270601013
|2 WOS
037 _ _ |a FZJ-2019-06502
100 1 _ |a Cavallaro, Gabriele
|0 P:(DE-Juel1)171343
|b 0
|e Corresponding author
|u fzj
111 2 _ |a IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
|c Yokohama
|d 2019-07-28 - 2019-08-02
|w Japan
245 _ _ |a Multi-Scale Convolutional SVM Networks for Multi-Class Classification Problems of Remote Sensing Images
260 _ _ |c 2019
|b IEEE
295 1 0 |a [Proceedings] - IEEE, 2019. - ISBN 978-1-5386-9154-0
300 _ _ |a 875-878
336 7 _ |a CONFERENCE_PAPER
|2 ORCID
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a INPROCEEDINGS
|2 BibTeX
336 7 _ |a conferenceObject
|2 DRIVER
336 7 _ |a Output Types/Conference Paper
|2 DataCite
336 7 _ |a Contribution to a conference proceedings
|b contrib
|m contrib
|0 PUB:(DE-HGF)8
|s 1576507707_30946
|2 PUB:(DE-HGF)
336 7 _ |a Contribution to a book
|0 PUB:(DE-HGF)7
|2 PUB:(DE-HGF)
|m contb
520 _ _ |a The classification of land-cover classes in remote sensing images can suit a variety of interdisciplinary applications suchas the interpretation of natural and man-made processes onthe Earth surface. The Convolutional Support Vector Machine (CSVM) network was recently proposed as binary classifier for the detection of objects in Unmanned Aerial Vehicle (UAV) images. The training phase of the CSVM isbased on convolutional layers that learn the kernel weightsvia a set of linear Support Vector Machines (SVMs). Thispaper proposes the Multi-scale Convolutional Support VectorMachine (MCSVM) network, that is an ensemble of CSVMclassifiers which process patches of different spatial sizes andcan deal with multi-class classification problems. The experiments are carried out on the EuroSAT Sentinel-2 dataset andthe results are compared to the one obtained with recent transfer learning approaches based on pre-trained ConvolutionalNeural Networks (CNNs).
536 _ _ |a 512 - Data-Intensive Science and Federated Computing (POF3-512)
|0 G:(DE-HGF)POF3-512
|c POF3-512
|f POF III
|x 0
536 _ _ |a DEEP-EST - DEEP - Extreme Scale Technologies (754304)
|0 G:(EU-Grant)754304
|c 754304
|f H2020-FETHPC-2016
|x 1
536 _ _ |a HBP - The Human Brain Project (604102)
|0 G:(EU-Grant)604102
|c 604102
|f FP7-ICT-2013-FET-F
|x 2
588 _ _ |a Dataset connected to CrossRef Conference
700 1 _ |a Bazi, Yakoub
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Melgani, Farid
|0 P:(DE-HGF)0
|b 2
700 1 _ |a Riedel, Morris
|0 P:(DE-Juel1)132239
|b 3
773 _ _ |a 10.1109/IGARSS.2019.8899831
856 4 _ |y OpenAccess
|u https://juser.fz-juelich.de/record/867900/files/MCSVM_Cavallaro.pdf
856 4 _ |y OpenAccess
|x pdfa
|u https://juser.fz-juelich.de/record/867900/files/MCSVM_Cavallaro.pdf?subformat=pdfa
909 C O |o oai:juser.fz-juelich.de:867900
|p openaire
|p open_access
|p driver
|p VDB
|p ec_fundedresources
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)171343
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)132239
913 1 _ |a DE-HGF
|b Key Technologies
|1 G:(DE-HGF)POF3-510
|0 G:(DE-HGF)POF3-512
|2 G:(DE-HGF)POF3-500
|v Data-Intensive Science and Federated Computing
|x 0
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
|l Supercomputing & Big Data
914 1 _ |y 2019
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
|x 0
980 _ _ |a contrib
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a contb
980 _ _ |a I:(DE-Juel1)JSC-20090406
980 1 _ |a FullTexts

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21