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https://github.com/nansencenter/sentinel1denoised

Thermal noise subtraction, scalloping correction, angular correction
https://github.com/nansencenter/sentinel1denoised

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Thermal noise subtraction, scalloping correction, angular correction

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# Sentinel1Denoised

Thermal noise subtraction, scalloping correction, angular correction



## Citation



If you use Sentinel1Denoised in any academic work please cite the following papers:



A. Korosov, D. Demchev, N. Miranda, N. Franceschi and J. -W. Park, "Thermal Denoising of Cross-Polarized Sentinel-1 Data in Interferometric and Extra Wide Swath Modes," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-11, 2022, Art no. 5218411, doi: 10.1109/TGRS.2021.3131036.



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.



## Installation

The software is written in Python and requires

[nansat](https://nansat.readthedocs.io/en/latest/source/installation.html)

and [scipy](https://www.scipy.org/install.html) packages. A simple way to install these packages

is to use [Anaconda](https://docs.conda.io/en/latest/miniconda.html).



```shell

# create conda environment with key requirements

conda env create -f environment.yml



# activate environment

conda activate s1denoise



# install s1denoise

pip install https://github.com/nansencenter/sentinel1denoised/archive/v1.4.0.tar.gz



```



Alternatively you can use [Docker](https://www.docker.com/):



```shell

# 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



```



## Example



Do processing inside Python environment:

```python

from s1denoise import Sentinel1Image

# open access to file with S1 data

input_file = '/path/to/data/S1B_EW_GRDM_1SDH_INPUTFILE.zip'

s1 = Sentinel1Image(input_file)



# 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 default NERSC algorithm

s0_hv = s1.remove_thermal_noise('HV')



# run thermal noise correction in HV polarisation with the  NERSC_TG algorithm applicable for old Sentinel-1 data

s0_hv = s1.remove_thermal_noise('HV', algorithm='NERSC_TG')



# run thermal and texture noise correction in HV polarisation

s0_hv = s1.remove_texture_noise('HV')



# High level function for processing both polarisations

from s1denoise.tools import run_correction

d = run_correction(input_file)



```



Process a single file with thermal, textural and angular correction and export in [dB] in a numpy file:



`s1_correction.py INPUTFILE.zip OUTPUTFILE.npz`



Process a single file and export as GeoTIFF (requires Nansat):



`s1_correction.py INPUTFILE.zip OUTPUTFILE.npz -g`



With option `-m` the script will also save landmask in Numpy format in a separate file with name `OUTPUTFILE.tif_mask.npz`:



`s1_correction.py INPUTFILE.zip OUTPUTFILE.tif -m`



Process a single file using Docker (replace `input_dir` and `output_dir` with actual directories):



`docker run --rm -v /data_dir:/data_dir s1denoise s1_correction.py /data_dir/INPUTFILE.zip /data_dir/OUPUTFILE.tif`



Note that for enabling the landmask functionality, you need to download and install the watermask:



```shell

# Set the environment variable MOD44WPATH to a writable directory. It is also used by the script.

export MOD44WPATH=/home/user/MOD44W



# Download the landmask and extract:

wget -nc -nv -P $MOD44WPATH https://github.com/nansencenter/mod44w/raw/master/MOD44W.tgz

tar -xzf $MOD44WPATH/MOD44W.tgz -C $MOD44WPATH/

rm $MOD44WPATH/MOD44W.tgz



```



## Experimental scripts



Sub-directories in `s1denoise/training` and `s1denoise/validation` contain scripts for training and validation of the noise scaling and power balancing coefficients and extra scaling. See README files in these sub-dirs for details.



## License

The project is licensed under the GNU general public license version 3.