+ Note from now on, the two testGeogrid scripts (testGeogridOptical.py and testGeogrid_ISCE.py) are only
+ hosted on the sister module autoRIFT's GitHub page (https://github.com/leiyangleon/autoRIFT).
+ Thus, they have been removed from this website.A Python module for precise mapping between (pixel index, pixel displacement) in imaging coordinates and (geolocation, motion velocity) in geographic Cartesian (northing/easting) coordinates
Geogrid 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, Geogrid must be installed with the InSAR Scientific Computing Environment (ISCE: https://github.com/isce-framework/isce2)
Geogrid 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 autoRIFT Python module (https://github.com/leiyangleon/autoRIFT). 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/leiyangleon/Geogrid
Piyush Agram (JPL/Caltech; piyush@gps.caltech.edu), Yang Lei (GPS/Caltech; ylei@caltech.edu; leiyangfrancis@gmail.com)
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
4. Demo
Please refer to the installation guide of autoRIFT repository (https://github.com/leiyangleon/autoRIFT) for installing the Geogrid module.