Feature extraction of hyperspectral images using boundary semilabeled 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.