A New Deep-Learning Approach for the Sub-Pixel Registration of Satellite Images Containing Sharp Displacement Discontinuities
Author:
Affiliation:
1. Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, IRD, Univ. Gustave Eiffel, ISTerre,Grenoble,France,38000
2. Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab,Grenoble,France,38000
Funder
Labex
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10281394/10281399/10283255.pdf?arnumber=10283255
Reference11 articles.
1. FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
2. Sub-pixel Optical Satellite Image Registration for Ground Deformation Using Deep Learning
3. Algorithms for the calculation of exact displacements, strains, and stresses for triangular dislocation elements in a uniform elastic half space
4. An efficient spatial domain technique for subpixel image registration
5. Extension of phase correlation to subpixel registration
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