With the latest missions launched by the European Space Agency (ESA) and National Aeronautics and Space Administration (NASA) equipped with the latest technologies in multi-spectral sensors, we face an unprecedented amount of data with spatial and temporal resolutions never reached before. Exploring the potential of this data with state-of-the-art AI techniques like Deep Learning, could potentially change the way we think about and protect our planet's resources.
This Docker container contains 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 ).
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!
: Lanaras, C., Bioucas-Dias, J., Galliani, S., Baltsavias, E., & Schindler, K. (2018). Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 305-319.
Run locally on your computer
You can run this module directly on your computer, assuming that you have Docker installed, by following these steps:
$ docker pull deephdc/deep-oc-satsr $ docker run -ti -p 5000:5000 deephdc/deep-oc-satsr
If you do not have Docker available or you do not want to install it, you can use udocker within a Python virtualenv:
$ virtualenv udocker $ source udocker/bin/activate $ git clone https://github.com/indigo-dc/udocker $ cd udocker $ pip install . $ udocker pull deephdc/deep-oc-satsr $ udocker create deephdc/deep-oc-satsr $ udocker run -p 5000:5000 deephdc/deep-oc-satsr
Once running, point your browser to
and you will see the API documentation, where you can test the module
functionality, as well as perform other actions (such as training).
For more information, refer to the user documentation.