In recent years, the demand for very high geometric resolution satellite images has increased significantly. The pan-sharpening techniques, which are part of the data fusion techniques, enable the increase in the geometric resolution of multispectral images using panchromatic imagery of the same scene. However, it is not trivial to choose a suitable pan-sharpening algorithm: there are several, but none of these is universally recognized as the best for any type of sensor, in addition to the fact that they can provide different results with regard to the investigated scene. This article focuses on the latter aspect: analyzing pan-sharpening algorithms in relation to different land covers. A dataset of GeoEye-1 images is selected from which four study areas (frames) are extracted: one natural, one rural, one urban and one semi-urban. The type of study area is determined considering the quantity of vegetation included in it based on the normalized difference vegetation index (NDVI). Nine pan-sharpening methods are applied to each frame and the resulting pan-sharpened images are compared by means of spectral and spatial quality indicators. Multicriteria analysis permits to define the best performing method related to each specific area as well as the most suitable one, considering the co-presence of different land covers in the analyzed scene. Brovey transformation fast supplies the best results among the methods analyzed in this study.