@article { author = {Imani, Maryam and Ghassemian, Hassan}, title = {Feature reduction of hyperspectral images: Discriminant analysis and the first principal component}, journal = {Journal of AI and Data Mining}, volume = {3}, number = {1}, pages = {1-9}, year = {2015}, publisher = {Shahrood University of Technology}, issn = {2322-5211}, eissn = {2322-4444}, doi = {10.5829/idosi.JAIDM.2015.03.01.01}, abstract = {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.}, keywords = {Discriminant analysis,Principal component,Feature reduction,Hyperspectral,Classification}, url = {https://jad.shahroodut.ac.ir/article_385.html}, eprint = {https://jad.shahroodut.ac.ir/article_385_8129a3985b54cdade8d7251e35fca4ff.pdf} }