SR4RS

An open source tool for super resolution. You can add your own models, cost functions, feel free to open a PR!

Cite

http://doi.org/10.5334/jors.369

@article{cresson2022sr4rs,
  title={SR4RS: A Tool for Super Resolution of Remote Sensing Images},
  author={Cresson, R{\'e}mi},
  journal={Journal of Open Research Software},
  volume={10},
  number={1},
  year={2022},
  publisher={Ubiquity Press}
}

Super Resolution for Remote Sensing

This work has been supported by the Programme National de Télédétection Spatiale (PNTS), grant n° PNTS-2020-07

Representative images

The following are Sentinel-2 images processed with a model trained from pansharpened Spot-6/7 images.

Read more

Blog post on MDL4EO

How to use?

SR4RS needs OTBTF>=2.3 to work.

Quick HR image generation using pre-trained model

1. Get the latest OTBTF docker image and enter the docker image. Here is an example with the otbtf 3.4 gpu image, using NVIDIA runtime:

docker run -ti --runtime=nvidia mdl4eo/otbtf:3.4.0-gpu bash

2. Download and unzip a pre-trained SavedModel (see this section to see available pre-trained models)

wget https://nextcloud.inrae.fr/s/boabW9yCjdpLPGX/download/sr4rs_sentinel2_bands4328_france2020_savedmodel.zip
unzip sr4rs_sentinel2_bands4328_france2020_savedmodel.zip

3. Clone SR4RS

git clone https://github.com/remicres/sr4rs.git

4. Use SR4RS to create an HR image (the considered pre-trained model runs on a Sentinel-2 image, 4-channels ordered as Red, Green, Blue, Near infrared). Just download a Sentinel-2 image from ESA hub or elsewhere, then concatenate the bands in this order (for that you can use the OTB application named otbcli_ConcatenateImages).

python sr4rs/code/sr.py \
--savedmodel sr4rs_sentinel2_bands4328_france2020_savedmodel \
--input /path/to/some/S2_image/stacked_channels_4328_10m.tif \
--output test.tif

Train a model

Here is a summary of the steps to follow.

1. Generate patches images using the PatchesExtraction application from OTBTF, from one low-res image (LR) and one high-res image (HR)
2. Run train.py on your patches images, and generate a SavedModel
3. Use sr.py on LR image using the previously generated SavedModel

For more details, see the documentation and check the pre-trained models.