Band selection for hyperspectral images using copulas-based mutual information

This paper explores a new measure for band selection of hyperspectral images using copulas-based mutual information. Mutual information offers a measure of the dependence between random variables, which can be used to select specific bands for the analysis of hyperspectral images. This is achieved by comparing mutual information values between the band images and a reference map. In this paper, copula density functions are exploited for the estimation of mutual information between the images. Due to the special relationship between copula density functions and joint probability density functions, copulas offer a natural and robust way for the estimation of the mutual information.