Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61246
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dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.creatorSamat, A-
dc.creatorGamba, P-
dc.creatorAbuduwaili, J-
dc.creatorLiu, S-
dc.creatorMiao, Z-
dc.date.accessioned2016-12-19T08:55:18Z-
dc.date.available2016-12-19T08:55:18Z-
dc.identifier.urihttp://hdl.handle.net/10397/61246-
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rights© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Samat, A., Gamba, P., Abuduwaili, J., Liu, S., & Miao, Z. (2016). Geodesic flow kernel support vector machine for hyperspectral image classification by unsupervised subspace feature transfer. Remote Sensing, 8(3), (Suppl. ), - is available athttps://dx.doi.org/10.3390/rs8030234en_US
dc.subjectDomain adaptationen_US
dc.subjectFeature transferen_US
dc.subjectGeodesic flow kernel support vector machineen_US
dc.subjectImage classificationen_US
dc.subjectRandomized nonlinear principal component analysisen_US
dc.subjectTransfer learningen_US
dc.titleGeodesic flow kernel support vector machine for hyperspectral image classification by unsupervised subspace feature transferen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume8-
dc.identifier.issue3-
dc.identifier.doi10.3390/rs8030234-
dcterms.abstractIn order to deal with scenarios where the training data, used to deduce a model, and the validation data have different statistical distributions, we study the problem of transformed subspace feature transfer for domain adaptation (DA) in the context of hyperspectral image classification via a geodesic Gaussian flow kernel based support vector machine (GFKSVM). To show the superior performance of the proposed approach, conventional support vector machines (SVMs) and state-of-the-art DA algorithms, including information-theoretical learning of discriminative cluster for domain adaptation (ITLDC), joint distribution adaptation (JDA), and joint transfer matching (JTM), are also considered. Additionally, unsupervised linear and nonlinear subspace feature transfer techniques including principal component analysis (PCA), randomized nonlinear principal component analysis (rPCA), factor analysis (FA) and non-negative matrix factorization (NNMF) are investigated and compared. Experiments on two real hyperspectral images show the cross-image classification performances of the GFKSVM, confirming its effectiveness and suitability when applied to hyperspectral images.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationRemote sensing, Mar. 2016, v. 8, no. 3, p. 1-23-
dcterms.isPartOfRemote sensing-
dcterms.issued2016-
dc.identifier.isiWOS:000373627400092-
dc.identifier.scopus2-s2.0-84962486372-
dc.identifier.eissn2072-4292-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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