This repository contains the Python code supporting the following paper:
- Forget Y., Shimoni M., Gilbert M., and Linard C. Complementarity Between Sentinel-1 and Landsat 8 Imagery for Built-Up Mapping in Sub-Saharan Africa, In Press, 2018.
Input and output datasets can be downloaded from Zenodo.
Python dependencies are listed in the environment.yml
and the requirements.txt
files.
A virtual environment containing all the required dependencies can be automatically created using conda
:
# Clone the repository
git clone https://github.com/yannforget/landsat-sentinel-fusion.git
cd landsat-sentinel-fusion
# Create the virtual environment
conda env create --file environment.yml
# Activate the environment
source activate landsat-sentinel-fusion
The code also depends on:
- Orfeo Toolbox for the computation of GLCM textures ;
- SNAP for SAR data preprocessing.
Input and output datasets are available in a Zenodo deposit.
# Download and decompress the data
wget -O data.zip https://zenodo.org/record/1450932/files/data.zip?download=1
unzip data.zip
Validation samples can be found in data/raw/reference
(as shapefiles) or in data/processed/reference
(as rasters).
Classification outputs and performance metrics are located in data/output
for each case study.
Due to storage constraints, input satellite imagery is not included in the Zenodo deposit. However, the product identifiers are available in data/raw/landsat/products.txt
and data/raw/sentinel-1/products.txt
. This means that they can be automatically downloaded using auxiliary software such as landsatxplore or sentinelsat.
For Landsat 8 scenes:
pip install landsatxplore
# Earth Explorer credentials
export LANDSATXPLORE_USERNAME=<your_username>
export LANDSATXPLORE_PASSWORD=<your_password>
cd data/raw/landsat
# Download each product with landsatxplore
for id in products.txt; do landsatxplore download $id; done
# Decompress each product
for product in *.zip; do unzip $product; done
For Sentinel-1 imagery:
cd ../sentinel-1
# Install and configure sentinelsat
pip install sentinelsat
export DHUS_USER=<your_username>
export DHUS_PASSWORD=<your_password>
# Download Sentinel-1 products
for id in products.txt; do sentinelsat --download --name $id; done
# Preprocessing of Optical and SAR data
python preprocess_landsat.py
python preprocess_sentinel1.py
# Dimensionality reduction (PCA) of SAR data
python dimreduction.py
# Random forest classification and validation
python classification.py
src/glcm.py
: computing of GLCM textures using Orfeo Toolbox.src/metadata.py
: accessing metadata specific to each case study.src/raster.py
: various raster processing functions.src/utils.py
: helper functions.
The following scripts has been used for the study but are not necessary to run the analysis :
src/aoi.py
: generates areas of interest for each case study.src/climate.py
: monthly ndvi and precipitations for each case study.src/land_masks.py
: land/water masks using openstreetmap data.src/preprocess_reference.py
: rasterizes reference samples (polygons).