A fast algorithm for feature extraction of hyperspectral images using the first order statistics

Abstract

A new supervised feature extraction method appropriate for small sample size situations is proposed in this work. The proposed method is based on the first-order statistics, in which there is no need to estimate the scatter matrices. Thus, the presented method not only can avoid the singularity problem in small sample size situations but also can achieve high performance in such situations. In addition, due to the fact that the proposed algorithm only exploits the first order statistical moments, it is very fast making it suitable for real-time hyperspectral scene analysis. The proposed method makes a matrix whose columns are obtained by averaging training samples of different classes. Then, a new transform is used to map the features from the original space into a new low-dimensional space such that the new features are as different from each other as possible. Subsequently, to capture the inherent nonlinearity of the original data, the algorithm is improved using the kernel trick. In experiments, four widely-used hyperspectral datasets, namely, Indian Pines, University of Pavia, Salinas, and Botswana are classified. The experimental results show that the proposed algorithm achieves state-of-the-art results in small sample size situations.