Multimodal image alignmentthrough a multiscale chain of neural networkswith application to remote sensing
Abstract. 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