Paper Info
Title
Super-Resolved Multi-Temporal Segmentation with Deep Permutation-Invariant Networks.
Abstract
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.
Year
DOI
Venue
2022
10.1109/IGARSS46834.2022.9884811
IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Diego Valsesia101.01
Enrico Magli242.50