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

v1.5.0 Updates:

1. autoRIFT/Geogrid now support processing Landsat 4, 5, 7, and 9 scene
2. autoRIFT/Geogrid now explicitly requires scenes to be in the same projection
3. autoRIFT will now use a default filter width of 5 pixels, except for Sentinel-1 scenes where it'll use the previous default of 21