In this paper we address the problem of matching two images with two different resolutions: a high-resolution image and a low-resolution one. On the premise that changes in resolution act as a smoothing equivalent to changes in scale, a scale-space representation of the high-resolution image is produced. Hence the one-to-one classical image matching paradigm becomes one-to-many because the lowresolution image is compared with all the scale-space representations of the high-resolution one. Key to the success of such a process is the proper representation of the features to be matched in scale-space. We show how to extract interest points at variable scales and we devise a method allowing the comparison of two images at two different resolutions. The method comprises the use of photometric- and rotationinvariant descriptors, a geometric model mapping the highresolution image onto a low-resolution image region, and an image matching strategy based on the robust estimation of this geometr...