Thermal noise subtraction, scalloping correction, angular correction
If you use Sentinel1Denoised in any academic work then you must cite the following paper:
Park, Jeong-Won; Korosov, Anton; Babiker, Mohamed; Sandven, Stein; and Won, Joong-Sun (2018): Efficient noise removal of Sentinel-1 TOPSAR cross-polarization channel, IEEE Transactions on Geoscience and Remote Sensing, 56(3), 1555-1565, doi:10.1109/TGRS.2017.2765248
Park, Jeong-Won; Won, Joong-Sun; Korosov, Anton A.; Babiker, Mohamed; and Miranda, Nuno (2019), Textural Noise Correction for Sentinel-1 TOPSAR Cross-Polarization Channel Images, IEEE Transactions on Geoscience and Remote Sensing, 57(6), 4040-4049, doi:10.1109/TGRS.2018.2889381
See the CITATION file for more information.
The software is written in Python and requires nansat and scipy packages. A simple way to install these packages is to use Anaconda.
# create conda environment with key requirements
conda create -y -n s1denoise gdal numpy pillow netcdf4 scipy requests
# activate environment
conda activate s1denoise
# install s1denoise
pip install https://github.com/nansencenter/sentinel1denoised/archive/v1.2.tar.gz
Alternatively you can use Docker:
# build an image with eveything installed
docker build . -t s1denoise
# run Python in container
docker run --rm -it -v /path/to/data:/path/to/data s1denoise python
Do processing inside Python environment:
from s1denoise import Sentinel1Image
# open access to file with S1 data
s1 = Sentinel1Image('/path/to/data/S1B_EW_GRDM_1SDH_INPUTFILE.zip')
# run thermal noise correction in HV polarisation with the default ESA algorithm
s0hve = s1.remove_thermal_noise('HV', algorithm='ESA')
# run thermal noise correction in HV polarisation with the NEW algorithm
s0_hv = s1.remove_thermal_noise('HV')
# run thermal and texture noise correction in HV polarisation
s0_hv = s1.remove_texture_noise('HV')
Process a single file with thermal, textural and angular correction and export in dB
s1_correction.py INPUTFILE.zip OUTPUTFILE.tif
Process a single file using Docker (replace input_dir and output_dir with actual directories):
docker run --rm -v /input_dir:/input_dir -v /output_dir:/output_dir s1denoise s1_correction.py /input_dir/INPUTFILE.zip /output_dir/OUPUTFILE.tif
Sub-directories in s1denoise/experimentalData contain scripts for training the noise scaling and power balancing coefficients and extra scaling.
See README files in these sub-dirs for details.
The project is licensed under the GNU general public license version 3.