In this work, we propose a deep-learning approach for aligning
cross-spectral images. Our approach utilizes a learned
descriptor invariant to different spectra. Multi-modal images
of the same scene capture different characteristics and therefore
their registration is challenging. To that end, we developed
a feature-based approach for registering visible (VIS)
to Near-Infra-Red (NIR) images. Our scheme detects corners
by Harris and matches them by a patch-metric learned
on top of a network trained using the CIFAR-10 dataset. As
our experiments demonstrate, we achieve accurate alignment
of cross-spectral images with sub-pixel accuracy. Comparing
to contemporary state-of-the-art, our approach is more accurate
in the task of VIS to NIR registration.