Band weighting and selection based on hyperplane margin maximization for hyperspectral image classification

Band selection is an effective solutions for dimensionality reduction in hyperspectral imagery. In this paper, a novel band weighting and selection method is proposed based on maximizing margin in support vector machine (SVM). The goal is to reduce high dimensionality if hyperspectral data while achieving accuracy classification performance. This method computes the weights of the samples to maximize the margin between the samples and the hyperplane in SVM. Bands are selected if they can enlarge the differences between classes and improve the classification performance. Experiments on two public benchmark hyperspectral datasets show the effectiveness of our method.