+ refined the workflow and ready for scaling the production of both optical and radar data results
+ improved memory use (by 50%) for autoRIFT and runtime (60x) for GeogridOptical
+ refined NDC filter to accommodate fine grid with spatially overlapping (dependent) search chips
+ parallel computing for NCC
+ support for remote input files using GDAL virtual file systems (e.g., `/vsicurl/https://...`)
+ see: https://gdal.org/user/virtual_file_systems.htmlA 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 (only supports Cartesian coordinates) either manually or as a conda install (https://github.com/conda-forge/autorift-feedstock). To allow support for both Cartesian and radar coordinates, autoRIFT must be installed with the InSAR Scientific Computing Environment (ISCE: https://github.com/isce-framework/isce2)
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 map-projected Cartesian (northing/easting) coordinate system 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 user-specified map-projected Cartesian (northing/easting) coordinate grid
NOTE: autoRIFT only returns displacement values for locations where significant feature matches are found, otherwise autoRIFT returns no data values.
Copyright (C) 2019 California Institute of Technology. Government Sponsorship Acknowledged.
Link: https://github.com/nasa-jpl/autoRIFT
Citation: https://doi.org/10.3390/rs13040749
Alex Gardner (JPL/Caltech; [email protected]) first described the algorithm "auto-RIFT" in (Gardner et al., 2018), developed the first version in MATLAB and continued to refine the algorithm;
Yang Lei (GPS/Caltech; [email protected]; [email protected]) translated it to Python, further optimized and incorporated to the ISCE software while also developed its sister module Geogrid;
Piyush Agram (GPS/Caltech; [email protected]) set up the installation as a standalone module and further cleaned the code.
Reference:
1. Gardner, A.S., Moholdt, G., Scambos, T., Fahnstock, M., Ligtenberg, S., Broeke, M.V.D. and Nilsson, J., 2018. Increased West Antarctic and unchanged East Antarctic ice discharge over the last 7 years. The Cryosphere, 12(2), pp.521-547.
2. [NEW] Lei, Y., Gardner, A. and Agram, P., 2021. Autonomous Repeat Image Feature Tracking (autoRIFT) and Its Application for Tracking Ice Displacement. Remote Sensing, 13(4), p.749.
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