Feature extraction of hyperspectral images using boundary semi-labeled samples and hybrid criterion
Feature extraction is a very important preprocessing step for classification of hyperspectral images. The linear discriminant analysis (LDA) method fails to work in small sample size situations. Moreover, LDA has poor efficiency for non-Gaussian data. LDA is optimized by a global criterion. Thus, it...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Shahrood University of Technology
2017-03-01
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| Series: | Journal of Artificial Intelligence and Data Mining |
| Subjects: | |
| Online Access: | http://jad.shahroodut.ac.ir/article_787_28f65de8f514c2553865ef5fca2c2ea4.pdf |