autoRIFT (autonomous Repeat Image Feature Tracking)

A Python module of a fast and intelligent algorithm for finding the pixel displacement between two images

autoRIFT can be installed as a standalone Python module (does not support radar coordinates) where both manual and conda installs (https://github.com/conda-forge/autorift-feedstock) are supported or with the InSAR Scientific Computing Environment (ISCE: https://github.com/isce-framework/isce2) software that supports handling Cartesian and radar coordinates

Use cases include all dense feature tracking applications, including the measurement of surface displacements occurring between two repeat satellite images as a result of glacier flow, large earthquake displacements, and land slides

autoRIFT can be used for dense feature tracking between two images over a grid defined in an arbitrary geographic Cartesian (northing/easting) coordinate projection when used in combination with the sister Geogrid Python module (https://github.com/leiyangleon/Geogrid). Example applications include searching radar-coordinate imagery on a polar stereographic grid and searching Universal Transverse Mercator (UTM) imagery at a specified geographic Cartesian (northing/easting) coordinate grid

Copyright (C) 2019 California Institute of Technology. Government Sponsorship Acknowledged.

Link: https://github.com/nasa-jpl/autoRIFT

Acknowledgement:

This effort was funded by the NASA MEaSUREs program in contribution to the Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) project (https://its-live.jpl.nasa.gov/) and through Alex Gardner’s participation in the NASA NISAR Science Team

v1.4.0 Updates:

1. refined the workflow and ready for scaling the production of both optical and radar data results
2. bug fixes
3. netCDF packaging for production, especially improved product quality for radar images