PyRate - a Python tool for InSAR Rate and Time-series Estimation

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PyRate is a Python tool for estimating the average rate (velocity) and incremental time-series of surface movement for every pixel in a stack of images generated by interferometric processing of Synthetic Aperture Radar (InSAR) data. It has been designed to use GAMMA or ROI_PAC format geocoded unwrapped interferograms.

PyRate is a partial Python translation of Pirate, a Matlab tool developed by the University of Leeds and the Guangdong University of Technology.

Home Page: https://github.com/GeoscienceAustralia/PyRate

Documentation: http://geoscienceaustralia.github.io/PyRate

Issue tracking: https://github.com/GeoscienceAustralia/PyRate/issues

Installation

Before you start, you will need to have a number of packages installed on your Linux system. These can either be installed directly onto the system, or you can use a virtual environment.

Direct Install

Make sure your system has the following packages installed. If not, run the command:

sudo apt-get install gdal-bin libgdal-dev libpng12-dev libblas-dev liblapack-dev libatlas-dev libatlas-base-dev gfortran libproj-dev openmpi-bin libopenmpi-dev netcdf-bin libnetcdf11 libnetcdf-dev

Virtual Environment

You can use one of the two virtual environment options below:

Note: if you are using a HPC environment, refer to these instructions instead.

Virtualenv

Install virtualenv.
You may need to install a slightly older python-daemon to install PyRate:

pip install python-daemon==2.1.1

Install PyRate by using one of the following options.

Run setup.py:

python setup.py install

Install the latest version of PyRate with pip from github:

pip install git+https://github.com/GeoscienceAustralia/PyRate

PyRate is also on pypi, the Python package manager. To install, run:

pip install Py-Rate

The Python requirements should automatically be built and installed.

If matplotlib does not install easily in a virtualenv, use this:

sudo apt-get -y build-dep matplotlib

Then re-install matplotlib in your virtualenv.

Anaconda

For using PyRate in an Anaconda environment use this guide.

Tests

A suite of tests have been developed for use in testing PyRate functionality and for further code development. The tests use pytest and can be found in the tests/ directory. A small test dataset is included in the tests/test_data/ directory.

To run the tests, use the following command inside the top level PyRate/ directory:

pip install pytest
cd PyRate
export PYRATEPATH=/path/to/PyRate
pytest tests/

Configuration File

Example configuration files for running PyRate with GAMMA or ROI_PAC format interferograms are contained in the configs/ directory.

PyRate Workflow

After following the steps under Installation, an executable program pyrate is created.

Use help for the different command line options:

>> pyrate --help
Usage: pyrate [OPTIONS] COMMAND [ARGS]...

Options:
  -v, --verbosity [DEBUG|INFO|WARNING|ERROR]
                                  Level of logging
  --help                          Show this message and exit.

Commands:
  linrate
  postprocess
  prepifg

The pyrate program has three command line options corresponding to different parts of the PyRate workflow:

prepifg
linrate
postprocess

Below we discuss these options.

1. prepifg: Preparing input interferograms

The first step of PyRate is to convert the GAMMA or ROI_PAC format unwrapped interferograms into geotiff format, followed by applying multi-looking and cropping operations. These procedures are all performed by the pyrate prepifg command:

>> pyrate prepifg --help
Usage: pyrate prepifg [OPTIONS] CONFIG_FILE

Options:
  --help  Show this message and exit.

The prepifg command is used as follows:

pyrate prepifg /path/to/config_file

The two major steps during the prepifg operation are described below.

Data formatting: convert to geotiff

The prepifg command will determine the input format from the value specified at the processor: keyword in the config file (0: ROI_PAC; 1: GAMMA)

Each GAMMA geocoded unwrapped interferogram requires three header files to extract metadata required for data formatting: a geocoded DEM header file (*.dem.par), and the master and slave epoch SLC parameter files (*.slc.par).

The path and name of the DEM header file are specified in the config file under the demHeaderFile: keyword.

The SLC parameter files should be in the directory specified in the config file under the slcFileDir: keyword. SLC parameter files for a particular interferogram are found automatically by date-string pattern matching.

Each ROI_PAC geocoded unwrapped interferogram requires its own header/resource file (*.unw.rsc). These header files need to be stored in the same directory as the interferograms.

In addition, the geocoded DEM header file (*.dem.rsc) is required and its path and name are specified in the config file under the demHeaderFile: keyword. The geographic projection in the parameter DATUM: is extracted from the DEM header file.

Image transformations: multi-looking and cropping

The prepifg command will also perform multi-looking (image sub-sampling) and cropping of the input interferograms.

Two example configuration files are provided in the configs/ directory, one each for ROI_PAC and GAMMA prepifg configuration. Either configuration file can be used with prepifg.

2. linrate: Main workflow and linear rate and time series analysis

>> pyrate linrate --help
Usage: pyrate linrate [OPTIONS] CONFIG_FILE

Options:
  -r, --rows INTEGER  divide ifgs into this many rows
  -c, --cols INTEGER  divide ifgs into this many columns
  --help              Show this message and exit

This is the core of the PyRate processing workflow, handled by the linrate command:

pyrate linrate path/to/config_file -c 3 -r 4

This command will perform the time series and linear rate analysis and has the option to break the interferograms into a number of tiles in r rows and c columns. For example, the above command will break the interferograms into 12 tiles and will produce 12 linear rate and time series products corresponding to each tile.

The optional rows and columns arguments can be used to create smaller tiles of the full size interferograms. This enables large interferograms to be more easily be accommodated in memory. The number of tiles chosen should be as small as possible that fits in the system memory.

Optionally, an orbital error correction and a spatio-temporal filter operation to estimate and remove atmospheric phase screen signals is applied to the interferograms prior to time series and linear rate analysis. The corrected interferograms are updated on disk and the corrections are not re-applied upon subsequent runs. This functionality is controlled by the orbfit: and apsest: options in the configuration file.

Non-optional pre-processing steps include: - Minimum Spanning Tree matrix calculation - Identification of a suitable reference pixel - Removal of reference phase from interferograms - Calculation of interferogram covariance - Assembly of the variance-covariance matrix

3. postprocess: Putting the tiles back together

The last step of the PyRate workflow is to re-assemble the tiles and save geotiff files of the final time series and linear rate products.

>> pyrate postprocess --help
Usage: pyrate postprocess [OPTIONS] CONFIG_FILE

Options:
  -r, --rows INTEGER  divide ifgs into this many rows
  -c, --cols INTEGER  divide ifgs into this many columns
  --help              Show this message and exit.

Make sure to use the same number of rows and columns that was used in the previous linrate step:

pyrate postprocess path/to/config_file -c 3 -r 4

MPI Support

PyRate has been designed for use on High Performance Computers and instructions to use a HPC cluster can be found in the hpc directory.

Python Multi-threading Support

In addition to the MPI support for HPC, PyRate can use standard multi-threading simply by turning parallel: 1 in the configuration file to take advantage of multiple cores on a single PC.

Bugs & Feedback

For bugs, questions and discussions, please use Github Issues.

License

PyRate is licensed under Apache License 2.0. See the License deed in this repository for details.

Contacts

Matt Garthwaite InSAR Team Lead, Geoscience Australia matt.garthwaite@ga.gov.au

Sudipta Basak Lead Developer basaks@gmail.com

License

bingo456/PyRate

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