Feature extraction based on tensor modelling for classification methods | |
Yan, Ronghua1,3; Peng, Jinye1,2; Ma, Dongmei4; Wen, Desheng3; Dong, Yingdi5 | |
2018-01-09 | |
会议日期 | 2017-10-23 |
会议地点 | Xian, China |
卷号 | 2018-January |
DOI | 10.1109/FADS.2017.8253205 |
页码 | 102-107 |
英文摘要 | Both spatial and spectral information is used when a hyperspectral image is modeled as a tensor. However, this model does not consider both the class and within-class information about the spectral features of ground objects. This means that further improving classification is very difficult. The authors propose that class information, within-class information, and pixels are selected to model a third-order tensor. The most important advantage of the proposed method is that all the pixels of one class are mapped to the same coefficient vector. Therefore, the within-class scatter is minimized, and the classification is improved when compared to the previous methods. © 2017 IEEE. |
产权排序 | 1 |
会议录 | Conference Proceedings - 2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017
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会议录出版者 | Institute of Electrical and Electronics Engineers Inc. |
语种 | 英语 |
ISBN号 | 9781538631485 |
内容类型 | 会议论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/30525] ![]() |
专题 | 西安光学精密机械研究所_空间光学应用研究室 |
通讯作者 | Yan, Ronghua |
作者单位 | 1.School of Electronics and Information, Northwestern Polytechnical University, Xi'an, China; 2.School of Information and Technology, Northwest University, Xi'an, China; 3.Xi'an Institute of Optics and Precision Mechanics, CAS, Xi'an, China; 4.Xi'an-Janssen Pharmaceutical Ltd., Xi'an, China; 5.School of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an, China |
推荐引用方式 GB/T 7714 | Yan, Ronghua,Peng, Jinye,Ma, Dongmei,et al. Feature extraction based on tensor modelling for classification methods[C]. 见:. Xian, China. 2017-10-23. |
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