%0 Journal Article %T Feature reduction of hyperspectral images: Discriminant analysis and the first principal component %J Journal of AI and Data Mining %I Shahrood University of Technology %Z 2322-5211 %A Imani, Maryam %A Ghassemian, Hassan %D 2015 %\ 01/01/2015 %V 3 %N 1 %P 1-9 %! Feature reduction of hyperspectral images: Discriminant analysis and the first principal component %K Discriminant analysis %K Principal component %K Feature reduction %K Hyperspectral %K Classification %R 10.5829/idosi.JAIDM.2015.03.01.01 %X 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. %U https://jad.shahroodut.ac.ir/article_385_8129a3985b54cdade8d7251e35fca4ff.pdf