# Super-resolved multi-temporal segmentation with deep permutation-invariant networks

arXiv: https://arxiv.org/abs/2204.02631 · HTML: https://mlsanity.com/paper/2204.02631/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 10 references that are in the map.
- First posted on arXiv 6 Apr 2022.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [DeepSUM: Deep neural network for Super-resolution of Unregistered Multitemporal images](https://mlsanity.com/paper/1907.06490/) (2019)
- [Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks](https://mlsanity.com/paper/2007.03107/) (2020)
- [Permutation invariance and uncertainty in multitemporal image super-resolution](https://mlsanity.com/paper/2105.12409/) (2021)

## Abstract (arXiv)

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.