Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/nasa-jpl/autoRIFT
A Python module of a fast and intelligent algorithm for finding the pixel displacement between two images
https://github.com/nasa-jpl/autoRIFT
Last synced: 2 months ago
JSON representation
A Python module of a fast and intelligent algorithm for finding the pixel displacement between two images
- Host: GitHub
- URL: https://github.com/nasa-jpl/autoRIFT
- Owner: nasa-jpl
- License: apache-2.0
- Created: 2019-08-06T18:26:41.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-07-25T20:02:56.000Z (6 months ago)
- Last Synced: 2024-08-03T01:25:44.773Z (6 months ago)
- Language: Python
- Homepage:
- Size: 12.6 MB
- Stars: 208
- Watchers: 18
- Forks: 52
- Open Issues: 15
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-rainmana - nasa-jpl/autoRIFT - A Python module of a fast and intelligent algorithm for finding the pixel displacement between two images (Python)
README
# autoRIFT (autonomous Repeat Image Feature Tracking)
[](https://www.python.org/)
[](https://github.com/leiyangleon/autoRIFT/releases)
[](https://github.com/leiyangleon/autoRIFT/blob/master/LICENSE)
[](https://doi.org/10.3390/rs13040749)
### Update Notes:
```diff
+ 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.html
```
**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 (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
## 1. Authors
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.**](https://doi.org/10.5194/tc-12-521-2018) *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.**](https://doi.org/10.3390/rs13040749) *Remote Sensing*, 13(4), p.749.
## 2. 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
## 3. [Features](/docs/features.md)
## 4. [Future Development](/docs/future.md)
## 5. [Demo](/docs/demo.md)
## 6. [Install](/docs/install.md)
## 7. [Instructions](/docs/instruction.md)