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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hui-Shan Chu Cheng-Hsuan Li Bor-Chen Kuo Chin-Teng Lin |
| Copyright Year | 2011 |
| Description | Author affiliation: Institute of Electrical Control Engineering, National Chiao Tung University, Hsinchu, Taiwan, R.O.C. (Bor-Chen Kuo; Chin-Teng Lin) || Graduate Institute of Educational Measurement and Statistics, National Taichung University of Education, Taichung, Taiwan, R.O.C. (Hui-Shan Chu; Cheng-Hsuan Li) |
| Abstract | Linear discriminant analysis (LDA) is a commonly used feature extraction (FE) method to resolve the Hughes phenomenon for classification. The Hughes phenomenon (also called the curse of dimensionality) is often encountered in classification when the dimensionality of the space grows and the size of the training set is fixed, especially in the small sampling size problem. Recent studies show that the spatial information can greatly improve the classification performance. Hence, for hyperspectral image classification, it is not only necessary to use the available spectral information but also to exploit the spatial information. In this paper, a semisupervised feature extraction method which is based on the scatter matrices of the fuzzy-type LDA and uses the semi-information is proposed. The experimental results on two hyperspectral images, the Washington DC Mall and the Indian Pine Site, show that the proposed method can yield a better classification performance than LDA in the small sampling size problem. |
| Starting Page | 1927 |
| Ending Page | 1932 |
| File Size | 612100 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424473151 |
| ISSN | 10987584 |
| e-ISBN | 9781424473175 |
| e-ISBN | 9781424473168 |
| DOI | 10.1109/FUZZY.2011.6007733 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-06-27 |
| Publisher Place | Taiwan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Feature extraction Training Hyperspectral imaging Nickel Accuracy Vegetation Linear discriminant analysis linear discriminate analysis feature extraction |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Artificial Intelligence Theoretical Computer Science Software |
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