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Multimodal Image Alignment Through a Multiscale Chain of Neural Networks with Application to Remote Sensing

  • Armand Zampieri
  • Guillaume Charpiat
  • Nicolas Girard
  • Yuliya Tarabalka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11220)

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.

Keywords

Multimodal Alignment Registration Remote sensing 

Notes

Acknowledgements

This work benefited from the support of the project EPITOME ANR-17-CE23-0009 of the French National Research Agency (ANR).

Supplementary material

474218_1_En_40_MOESM1_ESM.pdf (11.4 mb)
Supplementary material 1 (pdf 11643 KB)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Armand Zampieri
    • 1
  • Guillaume Charpiat
    • 2
  • Nicolas Girard
    • 1
  • Yuliya Tarabalka
    • 1
  1. 1.TITANE team, INRIAUniversité Côte d’AzurNiceFrance
  2. 2.TAU teamINRIA, LRI, Université Paris-SudOrsayFrance

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