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 able 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.
@article{arxiv.2204.02631,
title = {Super-resolved multi-temporal segmentation with deep permutation-invariant networks},
author = {Diego Valsesia and Enrico Magli},
journal= {arXiv preprint arXiv:2204.02631},
year = {2022}
}