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dc.contributor.authorJia, Sen
dc.contributor.authorDeng, Xianglong
dc.contributor.authorZhu, Jiasong
dc.contributor.authorXu, Meng
dc.contributor.authorZhou, Jun
dc.contributor.authorJia, Xiuping
dc.date.accessioned2019-09-30T03:03:46Z
dc.date.available2019-09-30T03:03:46Z
dc.date.issued2019
dc.identifier.issn0196-2892
dc.identifier.doi10.1109/tgrs.2019.2916329
dc.identifier.urihttp://hdl.handle.net/10072/387903
dc.description.abstractIn virtue of the spatial structural characteristic of surface materials, the performance of the hyperspectral image classification can be boosted by incorporating texture information. Normally, the spatial structure can be extracted by predefined operators, including the popular extended multiattribute profiles (EMAPs) and the Gabor filters. Recently, superpixel segmentation, which reflects the homogeneous regularity of objects, has drawn much attention in the field. In this paper, a collaborative representation-based multiscale superpixel fusion (CRMSF) approach has been proposed for the hyperspectral image classification. First, after obtaining the EMAPs from the raw hyperspectral image, a group of predesigned 3-D Gabor wavelet filters is convolved with the EMAP features, and the EMAP-Gabor features can, thus, be achieved. Second, the collaborative representation-based classification (CRC) is employed to fully and efficiently make use of the huge amount of extracted EMAP-Gabor features. Third, multiscale superpixel maps are generated from the EMAP features that are utilized to regularize the classification map obtained by CRC. A heuristic strategy has been specially devised to automatically decide the number of extracted superpixels in multiple scales, which can be perfectly compatible with hyperspectral images having various spatial sizes and spatial resolutions. This is the most important contribution of the developed CRMSF approach. Finally, the classification task is accomplished by fusing the multiple regularized classification maps. The CRMSF approach has been evaluated on four popular hyperspectral image data sets, and the experimental results show the advantages of CRMSF, particularly for a hyperspectral image with high spatial resolution.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartofpagefrom7770
dc.relation.ispartofpageto7784
dc.relation.ispartofissue10
dc.relation.ispartofjournalIEEE Transactions on Geoscience and Remote Sensing
dc.relation.ispartofvolume57
dc.subject.fieldofresearchGeophysics
dc.subject.fieldofresearchGeomatic engineering
dc.subject.fieldofresearchcode3706
dc.subject.fieldofresearchcode4013
dc.titleCollaborative Representation-Based Multiscale Superpixel Fusion for Hyperspectral Image Classification
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationJia, S; Deng, X; Zhu, J; Xu, M; Zhou, J; Jia, X, Collaborative Representation-Based Multiscale Superpixel Fusion for Hyperspectral Image Classification, IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(10), pp. 7770 - 7784
dc.date.updated2019-09-30T03:01:47Z
gro.hasfulltextNo Full Text
gro.griffith.authorZhou, Jun


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