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Spectral-Spatial Constraint Hyperspectral Image Classification
Ji, Rongrong1; Gao, Yue2; Hong, Richang3; Liu, Qiong4; Tao, Dacheng5,6; Li, Xuelong7
Department光学影像学习与分析中心
2014-03-01
Source PublicationIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN0196-2892
Volume52Issue:3Pages:1811-1824
AbstractHyperspectral image classification has attracted extensive research efforts in the recent decade. The main difficulty lies in the few labeled samples versus the high dimensional features. To this end, it is a fundamental step to explore the relationship among different pixels in hyperspectral image classification, toward jointly handing both the lack of label and high dimensionality problems. In the hyperspectral images, the classification task can be benefited from the spatial layout information. In this paper, we propose a hyperspectral image classification method to address both the pixel spectral and spatial constraints, in which the relationship among pixels is formulated in a hypergraph structure. In the constructed hypergraph, each vertex denotes a pixel in the hyperspectral image. And the hyperedges are constructed from both the distance between pixels in the feature space and the spatial locations of pixels. More specifically, a feature-based hyperedge is generated by using distance among pixels, where each pixel is connected with its K nearest neighbors in the feature space. Second, a spatial-based hyperedge is generated to model the layout among pixels by linking where each pixel is linked with its spatial local neighbors. Both the learning on the combinational hypergraph is conducted by jointly investigating the image feature and the spatial layout of pixels to seek their joint optimal partitions. Experiments on four data sets are performed to evaluate the effectiveness and and efficiency of the proposed method. Comparisons to the state-of-the-art methods demonstrate the superiority of the proposed method in the hyperspectral image classification.
SubtypeArticle
KeywordHypergraph Learning Hyperspectral Image Classification Spatial-constraint
WOS HeadingsScience & Technology ; Physical Sciences ; Technology
DOI10.1109/TGRS.2013.2255297
Indexed BySCI ; EI
WOS KeywordMORPHOLOGICAL PROFILES ; COMPONENT ANALYSIS ; FEATURE-SELECTION ; SVM ; RECOGNITION ; INFORMATION ; FEATURES ; BAND
Language英语
WOS Research AreaGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000329404800024
Citation statistics
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/22373
Collection光学影像学习与分析中心
Affiliation1.Xiamen Univ, Sch Informat Sci & Technol, Dept Cognit Sci, Xiamen 361005, Peoples R China
2.Natl Univ Singapore, Sch Comp, Singapore 117417, Singapore
3.Hefei Univ Technol, Hefei 230009, Peoples R China
4.Huazhong Univ Sci & Technol, Dept Elect & Informat Engn, Wuhan 430074, Peoples R China
5.Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, Sydney, NSW 2007, Australia
6.Univ Technol Sydney, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia
7.Chinese Acad Sci, Ctr OPT IMagery Anal & Learning, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China
Recommended Citation
GB/T 7714
Ji, Rongrong,Gao, Yue,Hong, Richang,et al. Spectral-Spatial Constraint Hyperspectral Image Classification[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2014,52(3):1811-1824.
APA Ji, Rongrong,Gao, Yue,Hong, Richang,Liu, Qiong,Tao, Dacheng,&Li, Xuelong.(2014).Spectral-Spatial Constraint Hyperspectral Image Classification.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,52(3),1811-1824.
MLA Ji, Rongrong,et al."Spectral-Spatial Constraint Hyperspectral Image Classification".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 52.3(2014):1811-1824.
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