Boundary based discriminant analysis for feature extraction in classification of hyperspectral images

For classification of hyperspectral images, particularly using limited training samples, supervised feature extraction is an approach for reduction of dimensionality, overcoming the Hughes phenomenon and increasing the classification accuracies. Classic and popular feature extraction methods such as linear discriminant analysis (LDA) have not good efficiency in small sample size situation because of the singularity problem. Another supervised method, nonparametric weighted feature extraction (NWFE) is efficient for solving some problems of LDA and works well using limited training samples. We propose an efficient approach for improving of discriminant analysis (DA) method. The proposed method, named boundary based discriminant analysis (BBDA), uses only the boundary training samples for DA to increases the classification accuracy. Moreover, with using a regularization technique, it overcomes the singularity problem in DA. The experimental results obtained on two popular real hyperspectral data sets (one agriculture image and one urban image) show the improvement of BBDA respect to some conventional supervised feature extraction methods in small sample size situation.