plfm-clouds-removal's Introduction

Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical Model

Authors: Alessandro Sebastianelli, Erika Puglisi, Maria Pia Del Rosso, Jamila Mifdal, Artur Nowakowki, Fiora Pirri, Pierre Philippe Mathieu and Silvia Liberata Ullo

PLEASE BE AWARE THAT THIS REPO IS CURRENTLY UNDER MAINTENANCE, WE ARE UPGRADING THE CODE A NEWER IMPLEMENTATION WILL APPEAR SOON

The proposed PLFM model combines a time-series of optical images and a SAR image to remove clouds from optical images.

Cloudy Image	Model Prediction	Ground Truth

Usage

To train the PLFM you can simply run

python main.py --train dataset_path

where dataset_path should contain 2 subfolders named "training" and "validation".

To change default parameters please look at models configuration file.

To test the PLFM you can simply run

python main.py --test dataset_path

where dataset_path is the path to the test dataset.

Dataset

The dataset will be available soon.

Cite our papers

The dataset has been created using our tool proposed in:

@article{sebastianelli2021automatic,
    title={Automatic dataset builder for Machine Learning applications to satellite imagery},
    author={Sebastianelli, Alessandro and Del Rosso, Maria Pia and Ullo, Silvia Liberata},
    journal={SoftwareX},
    volume={15},
    pages={100739},
    year={2021},
    publisher={Elsevier}
}

The PLFM is presented in

@article{sebastianelli2022clouds,
    author={Sebastianelli, Alessandro and Puglisi, Erika and Del Rosso, Maria Pia and Mifdal, Jamila and Nowakowski, Artur and Mathieu, Pierre Philippe and Pirri, Fiora and Ullo, Silvia Libearata},
    title={Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical Model},
    journal={Submitted to IEEE Transactions on Geoscience and Remote Sensing},
    publisher={IEEE},
    note = {arXiv preprint arXiv:2106.12226. https://arxiv.org/abs/2106.12226}
}

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