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We tackle here the problem of multimodal image non-rigid registration, whichis of prime importance in remote sensing and medical imaging. The difficultiesencountered by classical registration approaches include feature design andslow optimization by gradient descent. By analyzing these methods, we note thesignificance of the notion of scale. We design easy-to-train,fully-convolutional neural networks able to learn scale-specific features. Oncechained appropriately, they perform global registration in linear time, gettingrid of gradient descent schemes by predicting directly the deformation.We showtheir performance in terms of quality and speed through various tasks of remotesensing multimodal image alignment. In particular, we are able to registercorrectly cadastral maps of buildings as well as road polylines onto RGBimages, and outperform current keypoint matching methods.

Coarse to fine non-rigid registration: a chain of scale-specific neural networks for multimodal image alignment with application to remote sensing