feature extraction of hyperspectral images using boundary semi-labeled samples and hybrid criterion

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 a 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. in this work, we propose a new feature extraction method, which uses the boundary semi-labeled samples for solving small sample size problems. the proposed method, called the 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 one synthetic multi-spectral and three real hyperspectral images show the good efficiency of hfe-bsl compared to some popular and state-of-the-art feature extraction methods.