Shahrood University of Technology Journal of AI and Data Mining 2322-5211 5 1 2017 03 01 Feature extraction of hyperspectral images using boundary semi-labeled samples and hybrid criterion 39 53 787 10.22044/jadm.2017.787 EN M. Imani Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran. H. Ghassemian Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran. Journal Article 2016 04 27 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 is not sufficiently flexible to cope with the multi-modal distributed data. We propose a new feature extraction method in this paper, which uses the boundary semi-labeled samples for solving small sample size problem. The proposed method, which called hybrid feature extraction based on boundary semi-labeled samples (HFE-BSL), uses a hybrid criterion that integrates both the local and global criteria for feature extraction. Thus, it is robust and flexible. The experimental results with three real hyperspectral images show the good efficiency of HFE-BSL compared to some popular and state-of-the-art feature extraction methods. https://jad.shahroodut.ac.ir/article_787_28f65de8f514c2553865ef5fca2c2ea4.pdf