Though with a less dramatic growth compared to photogrammetry, remote sensing from multispectral imagery taken by UAV (Unmanned Aerial Vehicles) platforms is applied to vegetation health monitoring, crop management, water quality assessments, geological inspections and much more, with a sizeable number of multispectral cameras now available on the market. As for satellite images, a key point in remote sensing is calibration, both geometric and radiometric, and the modelling of disturbances, to get well co-registered reflectance data. Leaving aside radiometric calibration, this paper focuses on how to best georeference the different bands one with respect to the other. This is normally achieved by the so-called band-to-band registration (BBR). Here, a straightforward approach is proposed, that exploits the multi-camera geometry and, unlike BBR, all the information contents of the bands, as inter-band matches are searched (and possibly found) for every pair of bands and not only between a reference and a slave band. Tests on images taken with the 9-band MAIA S2 camera are presented, discussing the pro and cons of pre-calibration and on-the-job calibration of the camera parameters of each sensor. The results found show that the proposed method is at least as good as the BBR ones.