Shahrood University of Technology Journal of AI and Data Mining 2322-5211 3 1 2015 01 01 Feature reduction of hyperspectral images: Discriminant analysis and the first principal component 1 9 385 10.5829/idosi.JAIDM.2015.03.01.01 EN Maryam Imani Faculty of Electrical and Computer Engineering, Tarbiat Modares University Hassan Ghassemian Faculty of Electrical and Computer Engineering, Tarbiat Modares University Journal Article 2015 01 25 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. https://jad.shahroodut.ac.ir/article_385_8129a3985b54cdade8d7251e35fca4ff.pdf