  In this paper, we introduce a novel deep-learning method to align
cross-spectral images. Our approach relies on a learned descriptor which is
invariant to different spectra. Multi-modal images of the same scene capture
different signals and therefore their registration is challenging and it is not
solved by classic approaches. To that end, we developed a feature-based
approach that solves the visible (VIS) to Near-Infra-Red (NIR) registration
problem. Our algorithm detects corners by Harris and matches them by a
patch-metric learned on top of CIFAR-10 network descriptor. As our experiments
demonstrate we achieve a high-quality alignment of cross-spectral images with a
sub-pixel accuracy. Comparing to other existing methods, our approach is more
accurate in the task of VIS to NIR registration.
