Feature reduction of hyperspectral images: Discriminant analysis and the first principal component
When the number of training samples is limited, feature reduction plays an important role in classification of hyperspectral images. In this paper, we propose a supervised feature extraction method based on discriminant analysis (DA) which uses the first principal component (PC1) to weight the scatt...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Shahrood University of Technology
2015-01-01
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| Series: | Journal of Artificial Intelligence and Data Mining |
| Subjects: | |
| Online Access: | http://jad.shahroodut.ac.ir/article_385_8129a3985b54cdade8d7251e35fca4ff.pdf |
| _version_ | 1818430830816526336 |
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| author | Maryam Imani Hassan Ghassemian |
| author_facet | Maryam Imani Hassan Ghassemian |
| author_sort | Maryam Imani |
| collection | DOAJ |
| description | When the number of training samples is limited, feature reduction plays an important role in classification of hyperspectral images. In this paper, we propose a supervised feature extraction method based on discriminant analysis (DA) which uses the first principal component (PC1) to weight the scatter matrices. The proposed method, called DA-PC1, copes with the small sample size problem and has not the limitation of linear discriminant analysis (LDA) in the number of extracted features. In DA-PC1, the dominant structure of distribution is preserved by PC1 and the class separability is increased by DA. The experimental results show the good performance of DA-PC1 compared to some state-of-the-art feature extraction methods. |
| first_indexed | 2024-12-14T15:39:39Z |
| format | Article |
| id | doaj.art-f5882c7fca194793a3b7a561a7c6690f |
| institution | Directory Open Access Journal |
| issn | 2322-5211 2322-4444 |
| language | English |
| last_indexed | 2024-12-14T15:39:39Z |
| publishDate | 2015-01-01 |
| publisher | Shahrood University of Technology |
| record_format | Article |
| series | Journal of Artificial Intelligence and Data Mining |
| spelling | doaj.art-f5882c7fca194793a3b7a561a7c6690f2022-12-21T22:55:39ZengShahrood University of TechnologyJournal of Artificial Intelligence and Data Mining2322-52112322-44442015-01-01311910.5829/idosi.JAIDM.2015.03.01.01385Feature reduction of hyperspectral images: Discriminant analysis and the first principal componentMaryam Imani0Hassan Ghassemian1Faculty of Electrical and Computer Engineering, Tarbiat Modares UniversityFaculty of Electrical and Computer Engineering, Tarbiat Modares UniversityWhen the number of training samples is limited, feature reduction plays an important role in classification of hyperspectral images. In this paper, we propose a supervised feature extraction method based on discriminant analysis (DA) which uses the first principal component (PC1) to weight the scatter matrices. The proposed method, called DA-PC1, copes with the small sample size problem and has not the limitation of linear discriminant analysis (LDA) in the number of extracted features. In DA-PC1, the dominant structure of distribution is preserved by PC1 and the class separability is increased by DA. The experimental results show the good performance of DA-PC1 compared to some state-of-the-art feature extraction methods.http://jad.shahroodut.ac.ir/article_385_8129a3985b54cdade8d7251e35fca4ff.pdfDiscriminant analysisPrincipal componentFeature reductionHyperspectralClassification |
| spellingShingle | Maryam Imani Hassan Ghassemian Feature reduction of hyperspectral images: Discriminant analysis and the first principal component Journal of Artificial Intelligence and Data Mining Discriminant analysis Principal component Feature reduction Hyperspectral Classification |
| title | Feature reduction of hyperspectral images: Discriminant analysis and the first principal component |
| title_full | Feature reduction of hyperspectral images: Discriminant analysis and the first principal component |
| title_fullStr | Feature reduction of hyperspectral images: Discriminant analysis and the first principal component |
| title_full_unstemmed | Feature reduction of hyperspectral images: Discriminant analysis and the first principal component |
| title_short | Feature reduction of hyperspectral images: Discriminant analysis and the first principal component |
| title_sort | feature reduction of hyperspectral images discriminant analysis and the first principal component |
| topic | Discriminant analysis Principal component Feature reduction Hyperspectral Classification |
| url | http://jad.shahroodut.ac.ir/article_385_8129a3985b54cdade8d7251e35fca4ff.pdf |
| work_keys_str_mv | AT maryamimani featurereductionofhyperspectralimagesdiscriminantanalysisandthefirstprincipalcomponent AT hassanghassemian featurereductionofhyperspectralimagesdiscriminantanalysisandthefirstprincipalcomponent |