Integration of Segmentation Techniques for Classification of Hyperspectral Images

Benediktsson, Jon Atli; Ghamisi, Pedram; Couceiro, Micael S.; Fauvel, Mathieu

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dc.contributor	Háskóli Íslands
dc.contributor	University of Iceland
dc.contributor.author	orcid.org/0000-0003-0621-9647 Benediktsson, Jon Atli
dc.contributor.author	Ghamisi, Pedram
dc.contributor.author	Couceiro, Micael S.
dc.contributor.author	Fauvel, Mathieu
dc.date.accessioned	2016-09-30T15:45:31Z
dc.date.available	2016-09-30T15:45:31Z
dc.date.issued	2014
dc.identifier.citation	P. Ghamisi, M. S. Couceiro, M. Fauvel and J. A. Benediktsson. (2014). Integration of Segmentation Techniques for Classification of Hyperspectral Images. IEEE Geoscience and Remote Sensing Letters, 11(1), 342-346
dc.identifier.issn	1545-598X
dc.identifier.issn
dc.identifier.uri	https://hdl.handle.net/20.500.11815/138
dc.description.abstract	A new spectral-spatial method for classification of hyperspectral images is introduced. The proposed approach is based on two segmentation methods, fractional-order Darwinian particle swarm optimization and mean shift segmentation. The output of these two methods is classified by support vector machines. Experimental results indicate that the integration of the two segmentation methods can overcome the drawbacks of each other and increase the overall accuracy in classification.
dc.format.extent	342-346
dc.language.iso	en
dc.publisher	IEEE
dc.relation.ispartofseries	IEEE Geoscience and Remote Sensing Letters; 11(1)
dc.relation.ispartofseries
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Support vector machines
dc.subject	Geophysical image processing
dc.subject	Hyperspectral imaging
dc.subject	Image classification
dc.title	Integration of Segmentation Techniques for Classification of Hyperspectral Images
dcterms.license	(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works
dc.description.version	Ritrýnt tímarit
dc.identifier.journal	IEEE Geoscience and Remote Sensing Letters
dc.identifier.doi	10.1109/LGRS.2013.2257675
dc.contributor.department	Rafmagns- og tölvuverkfræðideild (HÍ)
dc.contributor.department	Faculty of Electrical and Computer Engineering (UI)
dc.contributor.school	Verkfræði- og náttúruvísindasvið (HÍ)
dc.contributor.school	School of Engineering and Natural Sciences (UI)