Multi-platform data introduce new possibilities in the context of data fusion, as they allow to exploit several remotely sensed images acquired by different combinations of sensors. This scenario is particularly interesting for the sharpening of hyperspectral (HS) images, due to the limited availability of high-resolution (HR) sensors mounted onboard of the same platform as that of the HS device. However, the differences in the acquisition geometry and the nonsimultaneity of this kind of observations introduce further difficulties whose effects have to be taken into account in the design of data fusion algorithms. In this study, we present the most widespread HS image sharpening techniques and assess their performances by testing them over real acquisitions taken by the Earth Observing-1 (EO-1) and the WorldView-3 (WV3) satellites. We also highlight the difficulties arising from the use of multi-platform data and, at the same time, the benefits achievable through this approach.