Classification algorithm of hyperspectral images based on kernel entropy analysis

To take advantage of the characteristics of KECA for hyperspectral remote sensing image classification, an approach of sample set selection and C-means classification is proposed. The sample selection is based on convex geometry concepts and C-means classification uses spectral angles as distance metrics in feature space. Experiment results of HYDICE hyperspectral data confirm that the proposed approach can improve classification accuracy effectively.