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Spectral-Spatial Constraint Hyperspectral Image Classification
文献类型：期刊论文
| 作者 | Ji, Rongrong1; Gao, Yue2; Hong, Richang3; Liu, Qiong4; Tao, Dacheng5,6 ; Li, Xuelong7
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| 刊名 | ieee transactions on geoscience and remote sensing
 |
| 出版日期 | 2014-03-01 |
| 卷号 | 52期号:3页码:1811-1824 |
| ISSN号 | 0196-2892 |
| 关键词 | Hypergraph learning hyperspectral image classification spatial-constraint |
| 英文摘要 | hyperspectral 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. |
| WOS标题词 | science & technology ; physical sciences ; technology |
| 类目[WOS] | geochemistry & geophysics ; engineering, electrical & electronic ; remote sensing ; imaging science & photographic technology |
| 研究领域[WOS] | geochemistry & geophysics ; engineering ; remote sensing ; imaging science & photographic technology |
| 关键词[WOS] | morphological profiles ; component analysis ; feature-selection ; svm ; recognition ; information ; features ; band |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000329404800024 |
| 公开日期 | 2015-03-18 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/22373]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 作者单位 | 1.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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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