Super-resolved multi-temporal segmentation with deep permutation-invariant networks [article]

Diego Valsesia, Enrico Magli
2022 arXiv   pre-print
Multi-image super-resolution from multi-temporal satellite acquisitions of a scene has recently enjoyed great success thanks to new deep learning models. In this paper, we go beyond classic image reconstruction at a higher resolution by studying a super-resolved inference problem, namely semantic segmentation at a spatial resolution higher than the one of sensing platform. We expand upon recently proposed models exploiting temporal permutation invariance with a multi-resolution fusion module
more » ... e to infer the rich semantic information needed by the segmentation task. The model presented in this paper has recently won the AI4EO challenge on Enhanced Sentinel 2 Agriculture.
arXiv:2204.02631v1 fatcat:wc4ngkj4fzbalnyvpnjo2vofwq