Multiclassification method for hyperspectral data based on Chernoff distance and pairwise decision tree strategy

Miao Zhang, Zheqi Lin, Yiming Cui, Fei Shen, Yi Shen

Abstract: To address the multi-classification problems of hyperspectral dataset, a new method with weighted kernel function based on Chernoff distance is proposed. Chernoff distance utilizes the information between categories and strengthens the separability of original dataset. The adjustable parameter in Chernoff distance can fit the hyperspectral dataset well compared with other least upper bounds. Pairwise decision tree reduces the number of subclassifiers that the dataset requires and improves the classification accuracy. The guidance of the weighed subclassifiers is global separability metric computed by Chernoff distance. Weighted subclassifiers highlight bands with more useful information and reduce accumulative error. Comparative experiment shows the effectiveness of the proposed method.