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dc.contributor.authorJia, S
dc.contributor.authorLiu, X
dc.contributor.authorXu, M
dc.contributor.authorYan, Q
dc.contributor.authorZhou, J
dc.contributor.authorJia, X
dc.contributor.authorLi, Q
dc.date.accessioned2021-05-27T03:19:58Z
dc.date.available2021-05-27T03:19:58Z
dc.date.issued2021
dc.identifier.issn0196-2892en_US
dc.identifier.doi10.1109/TGRS.2021.3073699en_US
dc.identifier.urihttp://hdl.handle.net/10072/404709
dc.description.abstractDomain adaptation, which cleverly applies the classifier learned from the source domain with sufficient labeled samples to the target domain with limited labeled samples, provides a feasible alternative to handle the small training sample problem of hyperspectral image (HSI) classification and has attracted much attention in the research field recently. Apparently, feature discriminative ability is vital for domain adaptation, which plays a crucial role during the migration process of transfer learning. In this article, a gradient feature-oriented 3-D domain adaptation (GF-3DDA) approach is proposed for HSI classification. First, 3-D Gabor is employed to remove noise from the original data, and two 2-D gradient-based features, 2-D Sobel gradient (SG) and 2-D derivative-of-Gaussian (DtG), are extended to the 3-D domain to coincide with the integrated spatial-spectral organization of HSI. Thus, the 3-D Sobel-Gabor gradient (3DSGG) and 3-D derivative-of-Gaussian-Gabor (3DDGG) features are achieved. Second, a 3-D domain adaptation method is implemented to jointly exploit the second- and fourth-order statistical descriptors in the spatial-spectral dimensions, which could effectively reduce domain shifts and thus achieve improved domain adaptation. Third, all the extracted domain-adapted feature modules are collaboratively classified by extreme learning machine (ELM), and the probability-like outputs of every ELM classifier are combined together to accomplish the classification task. Four hyperspectral data sets that each contains two scenes, i.e., Pavia, Shanghai-Hangzhou, Indiana, and Houston, are tested in the experiments. When only ten labeled samples per class are used in the target domain, the classification accuracies on four hyperspectral data sets achieved by our GF-3DDA approach are 93.31%, 84.35%, 69.32%, and 80.06%, respectively.en_US
dc.description.peerreviewedYesen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.ispartofjournalIEEE Transactions on Geoscience and Remote Sensingen_US
dc.subject.fieldofresearchGeophysicsen_US
dc.subject.fieldofresearchElectrical and Electronic Engineeringen_US
dc.subject.fieldofresearchGeomatic Engineeringen_US
dc.subject.fieldofresearchcode0404en_US
dc.subject.fieldofresearchcode0906en_US
dc.subject.fieldofresearchcode0909en_US
dc.titleGradient Feature-Oriented 3-D Domain Adaptation for Hyperspectral Image Classificationen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Articlesen_US
dcterms.bibliographicCitationJia, S; Liu, X; Xu, M; Yan, Q; Zhou, J; Jia, X; Li, Q, Gradient Feature-Oriented 3-D Domain Adaptation for Hyperspectral Image Classification, IEEE Transactions on Geoscience and Remote Sensing, 2021en_US
dc.date.updated2021-05-27T00:11:33Z
gro.description.notepublicThis publication has been entered as an advanced online version in Griffith Research Online.en_US
gro.hasfulltextNo Full Text
gro.griffith.authorZhou, Jun


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