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Hyperspectral image fusion is a key technique of hyperspectral data processing. In recent years, many fusion methods have been proposed, but there is little work concerning evaluation of the performances of different image fusion methods. In this paper, a method called quantitative correlation analysis (QCA) is proposed, which provides a quantitative measure of the information transferred by an image fusion technique into the output image. Using the proposed method, the performances of different image fusion methods can be compared and analyzed directly based on the images of before and after performing the fusion. The correlation information entropy, based on the developed QCA, is also proposed and testified by numerical simulations. Typical hyperspectral data are applied to the proposed method. The results show that the method is effective, and its conclusions agree with the classification results in applications.