RIPPL

The [R]adar [I]nterferometric [P]arallel [P]rocessing [L]ab is a python package for processing SAR SLC data and creating individual or stacks of InSAR data. The name refers to the fringe pattern or ripples that we see in InSAR products. The main user case is the creation of individual interferograms, or stacks of interferograms for specific areas. To ease the use of this package, the search and download of Sentinel-1 data is supported by the package.

Common InSAR products covered by this package are:

• Complex interferograms;
• Absolute coherence values;
• Radiomatic calibrated amplitude data;
• Unwrapped interferograms (based on external package of snaphu);
• Download and creation of SRTM and TanDEM-X DEMs
• Common SAR geometry values as incidence angles, off-nadir angles and heading;
• Baselines, height to phase values;
• Creation of projected and geocoded output grids.

To create InSAR products, it includes functions to:

• Geocode SAR images;
• Coregister SAR images;
• Calculate and apply earth and topographic phase corrections;
• Resampling of SAR data using different kernels.

Current support sensors:

• Sentinel-1.

Support on more sensors are planned.

Rationale

The rationale behind this software package is that we want to simplify the implementation of new processing steps as much as possible, while maintaining the processing speed needed for InSAR processing. This is also the reason why it is called the processing lab. At the same time, we want to make the connection between the processing package with post-processing operation as easy as possbile. Therefore, the data is outputted as projected geotiff images and the stack is easily searchable for results of specific processing steps and can be loaded as numpy memmap files, which gives access to the full stack while the use of active memory is limited.

The implementation of your own processing steps within the package is encouraged. This can be added to later versions of the package after testing.

Tutorial and examples

The RIPPL package includes both a tutorial and several examples. Please try the tutorial jupyter notebook first to get familiar with the processing. You can find it under .../tutorial/tutorial_Hawaii_earthquake_May_2018.ipynb.

To show a wider set of applications you can also check the examples folder, where a few different example cases are given.

Note that before you can run these examples you should prepare your python setup and the RIPPL package setup. See the next section for details.

Installation

1. Download RIPPL

git clone [email protected]:grsradartudelft/rippl.git $HOME/rippl

2. Install Dependencies

To keep your system tidy, we recommend creating a virtual environment (What is a virtual environment?). There are multiple ways of doing this, here we recommend conda and python venv.

a). Conda (recommended)

Step 1: Download miniconda

Linux (and Windows with Ubuntu subsystem):

# download latest miniconda3
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
chmod +x Miniconda3-latest-Linux-x86_64.sh
# install miniconda into your home directory
# by default it will be installed in $HOME/miniconda3. You can also specify a
# different directory to install miniconda to.
./Miniconda3-latest-Linux-x86_64 -b -p $HOME/miniconda
# conda initialization in bash
conda init bash

Mac OS

# download latest miniconda3
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
chmod +x Miniconda3-latest-MacOSX-x86_64.sh.sh
# install miniconda into your home directory
./Miniconda3-latest-MacOSX-x86_64.sh -b -p $HOME/miniconda
# If you are on macOS Catalina or later, the new default shell is zsh. If you're still using bash, then use bash instead of zsh when init.
conda init zsh

Step 2: Install dependencies

# create new virtual environment
RIPPL_PATH="$HOME/rippl"
# install packages defined in environment.yml inside the virtual environment.
conda env create -f $RIPPL_PATH/environment.yml

Step 3: Activate virtual environment

After the installation, all you need to do each time is to activate the virtual environment:

conda activate rippl

When you finished processing using RIPPL, you can deactivate the virtual environment by:

conda deactivate

b). Python venv

The steps should be identical for Mac OS, Linux and Windows (with ubuntu subsystem), as long as python3 is installed on your system. If you're not sure, do:

which python3

to check if you have python3 on your system path.

Step 1: Create virtual environment

Please make sure you have python>=3.6. Then do:

 python3 -m venv $HOME/.venv/rippl
 source $HOME/.venv/rippl/bin/activate

ℹ️ You can also use virtualenvwrapper or other virtual environment manage tools if you're familiar with those tools, but we will not elaborate here.

Step 2: Install gdal and other dependencies

⚠️ RIPPL uses gdal (what is gdal?) for now and it's python binding. Although it comes in handy, gdal is rather infamous for its complexity in installation. So before you start installing python dependencies, please make sure you have gdal installed on your system.

Ubuntu:

# https://mothergeo-py.readthedocs.io/en/latest/development/how-to/gdal-ubuntu-pkg.html
sudo add-apt-repository ppa:ubuntugis/ppa && sudo apt-get update
sudo apt-get update
sudo apt-get install gdal-bin
sudo apt-get install libgdal-dev
export CPLUS_INCLUDE_PATH=/usr/include/gdal
export C_INCLUDE_PATH=/usr/include/gdal

Mac OS (homebrew)

brew install gdal
# proj is required by cartopy module.
brew install proj

Check your gdal version:

gdal-config --version

Step 3: Install dependencies

pip install --upgrade pip
# change to your rippl directory if necessary.
pip install -e $HOME/rippl

If you run into compiling error while trying to install gdal, try the following:

# https://stackoverflow.com/questions/69123406/error-building-pygdal-unknown-distribution-option-use-2to3-fixers-and-use-2
pip3 install setuptools==57.5.0

Package setup

To start your first SAR processing using RIPPL you should also set your user and environment settings. You can find the installation code as a jupyter notebook .../rippl/user_setup.ipynb or as a regular python script .../rippl/user_setup.py. The latter can be run from the command line in the right folder:

python user_setup.py

This will set your accounts to download Sentinel-1 data and external DEMs and create a folder structure for the SAR, orbit, DEM and GIS database as well as a place to store your radar data_stacks.

The output of the setup scripts will be saved as a .txt file (.../rippl/user_settings.txt), which you can edit later on if you want to change your account or data folder settings.