Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/61246
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Land Surveying and Geo-Informatics | - |
dc.creator | Samat, A | - |
dc.creator | Gamba, P | - |
dc.creator | Abuduwaili, J | - |
dc.creator | Liu, S | - |
dc.creator | Miao, Z | - |
dc.date.accessioned | 2016-12-19T08:55:18Z | - |
dc.date.available | 2016-12-19T08:55:18Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/61246 | - |
dc.language.iso | en | en_US |
dc.publisher | Molecular 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.rights | The 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/rs8030234 | en_US |
dc.subject | Domain adaptation | en_US |
dc.subject | Feature transfer | en_US |
dc.subject | Geodesic flow kernel support vector machine | en_US |
dc.subject | Image classification | en_US |
dc.subject | Randomized nonlinear principal component analysis | en_US |
dc.subject | Transfer learning | en_US |
dc.title | Geodesic flow kernel support vector machine for hyperspectral image classification by unsupervised subspace feature transfer | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 8 | - |
dc.identifier.issue | 3 | - |
dc.identifier.doi | 10.3390/rs8030234 | - |
dcterms.abstract | In 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.accessRights | open access | en_US |
dcterms.bibliographicCitation | Remote sensing, Mar. 2016, v. 8, no. 3, p. 1-23 | - |
dcterms.isPartOf | Remote sensing | - |
dcterms.issued | 2016 | - |
dc.identifier.isi | WOS:000373627400092 | - |
dc.identifier.scopus | 2-s2.0-84962486372 | - |
dc.identifier.eissn | 2072-4292 | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Samat_Geodesic_Flow_Kernel.pdf | 10.17 MB | Adobe PDF | View/Open |
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