Subspace-Based Support Vector Machines for Hyperspectral Image Classification
文献类型：期刊论文
| 作者 | Gao, Lianru1; Li, Jun1; Khodadadzadeh, Mahdi1; Plaza, Antonio1; Zhang, Bing1; He, Zhijian1; Yan, Huiming1 |
| 刊名 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
 |
| 出版日期 | 2015 |
| 卷号 | 12期号:2 |
| 关键词 | Hyperspectral image classification multinomial logistic regression (MLR) subspace-based approaches support vector machines (SVMs) |
| 通讯作者 | Li, J (reprint author), Sun Yat Sen Univ, Sch Geog & Planning, Guangzhou 510275, Guangdong, Peoples R China. |
| 英文摘要 | Hyperspectral image classification has been a very active area of research in recent years. It faces challenges related with the high dimensionality of the data and the limited availability of training samples. In order to address these issues, subspace-based approaches have been developed to reduce the dimensionality of the input space in order to better exploit the (limited) training samples available. An example of this strategy is a recently developed subspace-projection-based multinomial logistic regression technique able to characterize mixed pixels, which are also an important concern in the analysis of hyperspectral data. In this letter, we extend the subspace-projection-based concept to support vector machines (SVMs), a very popular technique for remote sensing image classification. For that purpose, we construct the SVM nonlinear functions using the subspaces associated to each class. The resulting approach, called SVMsub, is experimentally validated using a real hyperspectral data set collected using the National Aeronautics and Space Administration's Airborne Visible/Infrared Imaging Spectrometer. The obtained results indicate that the proposed algorithm exhibits good performance in the presence of very limited training samples. |
| 研究领域[WOS] | Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000341567600027 |
| 源URL | [http://ir.ceode.ac.cn/handle/183411/38292]  |
| 专题 | 遥感与数字地球研究所_SCI/EI期刊论文_期刊论文 |
| 作者单位 | 1.[Gao, Lianru 2.Zhang, Bing 3.Yan, Huiming] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China 4.[Li, Jun 5.He, Zhijian] Sun Yat Sen Univ, Sch Geog & Planning, Guangzhou 510275, Guangdong, Peoples R China 6.[Li, Jun 7.He, Zhijian] Sun Yat Sen Univ, Guangdong Key Lab Urbanizat & Geosimulat, Guangzhou 510275, Guangdong, Peoples R China 8.[Khodadadzadeh, Mahdi 9.Plaza, Antonio] Univ Extremadura, Escuela Politecn, Dept Technol Comp & Commun, Hyperspectral Comp Lab, Caceres 10003, Spain |
| 推荐引用方式 GB/T 7714 | Gao, Lianru,Li, Jun,Khodadadzadeh, Mahdi,et al. Subspace-Based Support Vector Machines for Hyperspectral Image Classification[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2015,12(2). |
| APA | Gao, Lianru.,Li, Jun.,Khodadadzadeh, Mahdi.,Plaza, Antonio.,Zhang, Bing.,...&Yan, Huiming.(2015).Subspace-Based Support Vector Machines for Hyperspectral Image Classification.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,12(2). |
| MLA | Gao, Lianru,et al."Subspace-Based Support Vector Machines for Hyperspectral Image Classification".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 12.2(2015). |
入库方式： OAI收割
来源：遥感与数字地球研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。