A full-resolution training framework for Sentinel-2 image fusion (ArXiv) is a deep learning method for Pansharpening based on unsupervised and full-resolution framework training.
If you use FR-FUSE in your research, please use the following BibTeX entry.
@inproceedings{Ciotola2021,
author={Ciotola, Matteo and Ragosta, Mario and Poggi, Giovanni and Scarpa, Giuseppe},
booktitle={2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS},
title={A Full-Resolution Training Framework for Sentinel-2 Image Fusion},
year={2021},
volume={},
number={},
pages={1260-1263},
doi={10.1109/IGARSS47720.2021.9553199}
}
Copyright (c) 2021 Image Processing Research Group of University Federico II of Naples ('GRIP-UNINA'). All rights reserved. This software should be used, reproduced and modified only for informational and nonprofit purposes.
By downloading and/or using any of these files, you implicitly agree to all the
terms of the license, as specified in the document LICENSE
(included in this package)
All the functions and scripts were tested on Windows and Ubuntu O.S., with these constraints:
- Python 3.10.10
- PyTorch 2.0.0
- Cuda 11.7 or 11.8 (For GPU acceleration).
the operation is not guaranteed with other configurations.
- Install Anaconda and git
- Create a folder in which save the algorithm
- Download the algorithm and unzip it into the folder or from CLI:
git clone https://github.com/matciotola/FR-FUSE
- Create the virtual environment with the fr_fuse_environment.yml`
conda env create -n fr_fuse_env -f fr_fuse_environment.yml
- Activate the Conda Environment
conda activate fr_fuse_env
- Test it
python main.py -b10 example/10/New_York.tiff -b20 example/20/New_York.tiff -o ./Output_Example