Autonomous Repeat Image Feature Tracking (autoRIFT) and Its Application for Tracking Ice Displacement

In this paper, we build on past efforts with regard to the implementation of an efficient feature tracking algorithm for mass processing satellite images. This generic open-source routine can be applied any type imagery measure sub-pixel displacements between The consists a module (autoRIFT) that enhances computational efficiency and geocoding (Geogrid) mitigates problems found in existing algorithms. When imagery, autoRIFT run grid native image coordinates (such as radar or map) and, when used conjunction Geogrid module, user-defined geographic Cartesian such Universal Transverse Mercator Polar Stereographic. To validate accuracy approach, demonstrate its use ice motion by using ESA’s Sentinel-1A/B data (seven pairs) NASA’s Landsat-8 optical collected over Greenland’s Jakobshavn Isbræ glacier 2017. Feature-tracked velocity errors are characterized stable surfaces, where best pair 6 day separation has X/Y 12 m/year 39 m/year, compared 22 31 16-day separation. Different error sources pairs investigated, seasonal variation dependence temporal baseline analyzed. Estimated velocities were reference derived from DLR’s TanDEM-X SAR/InSAR fast-moving outlet, Sentinel-1 results agree within 4% 3–7% Landsat-8. A comprehensive apples-to-apples comparison is made runtime multiple implementations proposed widely-used “dense ampcor" program NASA/JPL’s ISCE software. shown provide two orders magnitude improvement 20% accuracy.