Home > Publications database > Facilitating efficient data analysis of remotely sensed images using standards-based parameter sweep models > print

001     842290
005     20210129232245.0
024 7 _ |a 10.1109/IGARSS.2017.8127797
|2 doi
037 _ _ |a FZJ-2018-00537
100 1 _ |a Memon, Mohammad Shahbaz
|0 P:(DE-Juel1)132190
|b 0
|e Corresponding author
111 2 _ |a 2017 IEEE International Geoscience and Remote Sensing Symposium
|g IGARSS 2017
|c Fort Worth, TX
|d 2017-07-23 - 2017-07-28
|w USA
245 _ _ |a Facilitating efficient data analysis of remotely sensed images using standards-based parameter sweep models
260 _ _ |c 2017
|b IEEE
295 1 0 |a [Proceedings] - IEEE, 2017. - ISBN 978-1-5090-4951-6
300 _ _ |a 3680-3683
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 1516869462_22606
|2 PUB:(DE-HGF)
336 7 _ |a Contribution to a book
|0 PUB:(DE-HGF)7
|2 PUB:(DE-HGF)
|m contb
520 _ _ |a Classification of remote sensing images often use Support Vector Machines (SVMs) that require an n-fold cross-validation phase in order to do model selection. This phase is characterized by sweeping through a wide set of parameter combinations of SVM kernel and cost parameters. As a consequence this process is computationally expensive but represents a principled way of tuning a model for better accuracy and to prevent overfitting together with regularization that is in SVMs inherently solved in the optimization. Since the cross-validation technique is done in a principled way also known as ‘gridsearch’, we aim at supporting remote sensing scientists in two ways. Firstly by reducing the time-to-solution of the cross-validation by applying state-of-the-art parallel processing methods because the sweep of parameters and cross-validation runs itself can be nicely parallelized. Secondly by reducing manual labour by automating the parallel submission processes since manually performing cross-validation is very time consuming, unintuitive, and error-prone especially in large-scale cluster or supercomputing environments (e.g., batch job scripts, node/core/task parameters, etc.).
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
588 _ _ |a Dataset connected to CrossRef Conference
650 2 7 |a Others
|0 V:(DE-MLZ)SciArea-250
|2 V:(DE-HGF)
|x 0
700 1 _ |a Cavallaro, Gabriele
|0 P:(DE-Juel1)171343
|b 1
|u fzj
700 1 _ |a Riedel, Morris
|0 P:(DE-Juel1)132239
|b 2
|u fzj
700 1 _ |a Neukirchen, Helmut
|0 P:(DE-Juel1)169980
|b 3
773 _ _ |a 10.1109/IGARSS.2017.8127797
909 C O |o oai:juser.fz-juelich.de:842290
|p VDB
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)132190
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)171343
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|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 2017
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 contb
980 _ _ |a I:(DE-Juel1)JSC-20090406
980 _ _ |a UNRESTRICTED

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21