  We tackle here the problem of multimodal image non-rigid registration, which
is of prime importance in remote sensing and medical imaging. The difficulties
encountered by classical registration approaches include feature design and
slow optimization by gradient descent. By analyzing these methods, we note the
significance of the notion of scale. We design easy-to-train,
fully-convolutional neural networks able to learn scale-specific features. Once
chained appropriately, they perform global registration in linear time, getting
rid of gradient descent schemes by predicting directly the deformation.We show
their performance in terms of quality and speed through various tasks of remote
sensing multimodal image alignment. In particular, we are able to register
correctly cadastral maps of buildings as well as road polylines onto RGB
images, and outperform current keypoint matching methods.
