Determining the accuracy of pan-sharpening programs is rather subjective because it has been largely used for enhancing visual analysis. However, there is some research on one such program--NNDiffuse Pan-Sharpening--that tested the expected effectiveness of NNDiffuse using the standard spectral methods of Euclidean Distance and Spectral Angle Mapper. In this project, we extend those results to test how accurate NNDiffuse is in practice through its effect on the accuracy of image classification. NNDiffuse was applied to a synthetic image where perfect truth of the scene content is known. Different strategies for identifying training and testing pixels for the unsharpened and sharpened images were defined and assessed to quantify the effects of NNDiffuse on the accuracy of image classification. Application of these strategies are expected to improve land cover classification results using pan-sharpened images.