This repository contains the PyTorch code for the paper:
The color out of space: learning self-supervised representations for Earth Observation imagery
- Python >= 3.7
- PyTorch >= 1.5
- CUDA 10.0
We adopt the BigEarthNet Dataset. Refer to the README in the Colorization\dataset and Multi_label_classification\dataset folders for further information.
Different Encoder-Decoder combinations are available
- Encoder ResNet18 - Decoder ResNet18
- Encoder ResNet50 - Decoder ResNet50
- Encoder ResNet50 - Decoder ResNet18
The same encoders were employed in the colorization phase and an Ensemble model, composed of two equal encoders trained respectively on RGB and all other bands.
Before running the files main.py contained in both the Colorization and Multi_label_classification folders you can set the desired parameters in the file job_config.py, which modify the ones contained in config/configuration.json.
If you have any questions, please contact [email protected], or open an issue on this repo.
If you find this repository useful for your research, please cite the following paper:
@inproceedings{vincenzi2020color,
title={The color out of space: learning self-supervised representations for Earth Observation imagery},
author={Vincenzi, Stefano and Porrello, Angelo and Buzzega, Pietro and Cipriano, Marco and Pietro, Fronte and Roberto, Cuccu and
Carla, Ippoliti and Annamaria, Conte and Calderara, Simone},
booktitle={25th International Conference on Pattern Recognition},
year={2020}
}