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A project to perform super-resolution on multispectral images from any satellite
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DEEP Open Catalogue: satsr

Build Status

Author/Mantainer: Ignacio Heredia (CSIC)

Project: This work is part of the DEEP Hybrid-DataCloud project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777435.

This is a plug-and-play tool to perform super-resolution on satellite imagery. It uses Deep Learning to provide a better performing alternative to classical pansharpening (more details in the paper mentioned below).

Right now we are supporting super-resolution for the following satellites:

More information on the satellites and processing levels that are supported can be found here along with some demo images of the super-resolutions performed in non-training data. If you want to perform super-resolution on another satellite, go to the training section to see how you can easily add support for additional satellites. We are happy to accept PRs! 🚀

You can find more information about it in the DEEP Marketplace.


Installing this module

Local installation


  • It is a requirement to have Tensorflow>=1.12.0 installed (either in gpu or cpu mode). This is not listed in the requirements.txt as it breaks GPU support.

  • This package needs the GDAL library (version >2.4.1). You can either install it with conda (with conda install gdal) or install it with pip after having installed some additional external libraries. You can install those libraries in Linux with:

    sudo add-apt-repository -y ppa:ubuntugis/ubuntugis-unstable
    sudo apt update
    sudo apt install -y gdal-bin python-gdal python3-gdal
  • This project has been tested in Ubuntu 18.04 with Python 3.6.5. Further package requirements are described in the requirements.txt file.

To start using this framework run:

git clone
cd satsr
pip install -e .

To use this module with an API you have to install the DEEPaaS package (temporarily, until 1.0 launching, you will have to use the test-args branch):

git clone -b test-args
cd deepaas
pip install -e .

and run deepaas-run --listen-ip Now open and look for the methods belonging to the satsr module.

Docker installation

We have also prepared a ready-to-use Docker container to run this module. To run it:

docker search deephdc
docker run -ti -p 5000:5000 deephdc/deep-oc-satsr

Now open and look for the methods belonging to the satsr module.

Train other satellites

If you have images from a satellite that is not currently supported you can easily add support for your satellite:

  • Go to ./satsr/satellites and create a file. This file should contains basic information like resolutions, bands names and functions for opening the bands. Check the ./satsr/ for a reference on what parameters and functions have to be defined.
  • Optional: You can also create another file like to support downloading data directly with Python (see ./satsr/
  • Link you newly created files with the satellite names by modifying the file ./satsr/
  • Download training data (you can use the file ./satsr/ for convenience).
  • Create in ./data/dataset_files a train.txt file with the tile names of the folders you want to train with. You can also create a val.txt if you want to use validation during training.
  • Run the TRAIN method in the DEEPaaS API with your training configuration. You can monitor the progress of the training using Tensorboard by going to .
  • Rename the output timestamped folder in ./models to something like mynewsat_model_*m.

Now you proceed to the next section to use you newly trained model to perfom super-resolution. If you are happy with the performance of your model we accept PRs to add it to the catalogue! In the near future we'll be happy to add support for additional Lansat and Sentinel missions, along with additional processing levels for satellites that are already supported, as well as any other satellite imagery in the public domain like ASTER or MeteoSat.

Perform super-resolution

There are two possible ways to use the PREDICT method from the DEEPaaS API:

  • supply to the data argument a path pointing to a compressed file (zip or tarball) containing your satellite tile.
  • supply to the url argument an online url of a compressed file (zip or tarball) containing your satellite tile. Here is an example of such an url for the Sentinel-2 L2A that you can use for testing purposes. You can find other sample url for other satellites here.


The code in this project is based on the original repo by Charis Lanaras of the paper Super-Resolution of Sentinel-2 Images: Learning a Globally Applicable Deep Neural Network.

The main changes with respect to the original repo are that:

  • most of the code has been either rewritten, restructured or cleaned up for better modularity, in order to make it plug-and-playable with other satellites (like LandSat).
  • the code has been packaged into an installable Python package.
  • it has been made compatible with the DEEPaaS API.
  • some minor bugs have been corrected (and contributed back into the original repo in #5 and #6).

If you consider this project to be useful, please consider citing the original paper:

  title={Super-Resolution of Sentinel-2 Images: Learning a Globally Applicable Deep Neural Network},
  author={Lanaras, Charis and Bioucas-Dias, Jos{\'e} and Galliani, Silvano and Baltsavias, Emmanuel and Schindler, Konrad},
  journal={arXiv preprint arXiv:1803.04271},
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