- tensorflow-gpu (or tensorflow)
- matplotlib (optional)
- GDAL >= 2.2 (optional)
See the detailed description in the
training directory. Use the
--resume option with your application related Sentinel-2 tiles to refine the provided network weights.
Using the Trained Network
The network can be used directly on downloaded Sentinel-2 tiles. See details in the
s2_tiles_supres.py file. An example follows:
python s2_tiles_supres.py /path/to/S2A_MSIL1C_20161230T074322_N0204_R092_T37NCE_20161230T075722.SAFE/MTD_MSIL1C.xml /path/to/output_file.tif --roi_x_y "100,100,2000,2000"
Point to the
.xml file of the uzipped S2 tile. You must also provide an output file -- consider using a
.tif extension that is easily read by QGIS. If you want to also copy the high resolution (10m bands) you can do so, with the option
To also predict the lowest resolution bands (60m) use the
The demo is also ported to MATLAB:
demoDSen2.m. However, MATLAB 2017b or newer is needed to run. It utilizes the Neural Network toolbox that can be accelerated with the Parallel Computing Toolbox.
Used Sentinel-2 tiles
The Sentinel-2 tiles used for training and testing are listed in:
They can be downloaded from the Copernicus Open Access Hub.