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dc.contributor.authorJia, Sen
dc.contributor.authorZhuang, Jiayue
dc.contributor.authorDeng, Lin
dc.contributor.authorZhu, Jiasong
dc.contributor.authorXu, Meng
dc.contributor.authorZhou, Jun
dc.contributor.authorJia, Xiuping
dc.date.accessioned2019-08-27T04:17:57Z
dc.date.available2019-08-27T04:17:57Z
dc.date.issued2019
dc.identifier.issn0196-2892
dc.identifier.doi10.1109/tgrs.2019.2923213
dc.identifier.urihttp://hdl.handle.net/10072/386728
dc.description.abstractHyperspectral remote sensing imagery provides valuable and rich information to distinguish the characteristics of materials. However, this advantage of hyperspectral imagery often encounters the problem of a limited amount of training samples, which is caused by the difficulty of manually labeling. Fortunately, the spatial distribution of surface objects can be integrated with the spectral signature to improve the discriminative ability. In this paper, a 3-D Gaussian-Gabor feature extraction and selection framework has been proposed for hyperspectral image classification. First, a bank of 3-D Gaussian-Gabor filters are convolved with the concatenated data of both extended multi-attribute profile (EMAP) features and raw hyperspectral data. Second, an improved fast density peak clustering (IFDPC) method is introduced to select the most representative features from each extracted 3-D Gaussian-Gabor feature cube. Finally, the retained features are combined together to accomplish the classification task. The proposed method is thus named as GG-IFDPC. Three real hyperspectral imagery data sets have been utilized, and the experiments demonstrate the advantages of the proposed GG-IFDPC approach over the compared ones.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartofpagefrom1
dc.relation.ispartofpageto14
dc.relation.ispartofjournalIEEE Transactions on Geoscience and Remote Sensing
dc.subject.fieldofresearchGeophysics
dc.subject.fieldofresearchGeomatic engineering
dc.subject.fieldofresearchcode3706
dc.subject.fieldofresearchcode4013
dc.title3-D Gaussian-Gabor Feature Extraction and Selection for Hyperspectral Imagery Classification
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationJia, S; Zhuang, J; Deng, L; Zhu, J; Xu, M; Zhou, J; Jia, X, 3-D Gaussian-Gabor Feature Extraction and Selection for Hyperspectral Imagery Classification, IEEE Transactions on Geoscience and Remote Sensing, 2019, pp. 1-14
dc.date.updated2019-08-27T04:15:07Z
gro.description.notepublicThis publication has been entered into Griffith Research Online as an Advanced Online Version.
gro.hasfulltextNo Full Text
gro.griffith.authorZhou, Jun


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