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 the mass processing of satellite images. This generic open-source feature tracking routine can be applied to any type of imagery to measure sub-pixel displacements between images....
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ftdoajarticles:oai:doaj.org/article:d677c9b8ceda42e5a67cb4b3dc253908 2024-01-14T10:08:13+01:00 Autonomous Repeat Image Feature Tracking (autoRIFT) and Its Application for Tracking Ice Displacement Yang Lei Alex Gardner Piyush Agram 2021-02-01T00:00:00Z https://doi.org/10.3390/rs13040749 https://doaj.org/article/d677c9b8ceda42e5a67cb4b3dc253908 EN eng MDPI AG https://www.mdpi.com/2072-4292/13/4/749 https://doaj.org/toc/2072-4292 doi:10.3390/rs13040749 2072-4292 https://doaj.org/article/d677c9b8ceda42e5a67cb4b3dc253908 Remote Sensing, Vol 13, Iss 4, p 749 (2021) feature tracking optical radar satellite imagery surface displacement glacier velocity Science Q article 2021 ftdoajarticles https://doi.org/10.3390/rs13040749 2023-12-17T01:45:39Z In this paper, we build on past efforts with regard to the implementation of an efficient feature tracking algorithm for the mass processing of satellite images. This generic open-source feature tracking routine can be applied to any type of imagery to measure sub-pixel displacements between images. The routine consists of a feature tracking module (autoRIFT) that enhances computational efficiency and a geocoding module (Geogrid) that mitigates problems found in existing geocoding algorithms. When applied to satellite imagery, autoRIFT can run on a grid in the native image coordinates (such as radar or map) and, when used in conjunction with the Geogrid module, on a user-defined grid in geographic Cartesian coordinates such as Universal Transverse Mercator or Polar Stereographic. To validate the efficiency and accuracy of this approach, we demonstrate its use for tracking ice motion by using ESA’s Sentinel-1A/B radar data (seven pairs) and NASA’s Landsat-8 optical data (seven pairs) collected over Greenland’s Jakobshavn Isbræ glacier in 2017. Feature-tracked velocity errors are characterized over stable surfaces, where the best Sentinel-1A/B pair with a 6 day separation has errors in X / Y of 12 m/year or 39 m/year, compared to 22 m/year or 31 m/year for Landsat-8 with a 16-day separation. Different error sources for radar and optical image pairs are investigated, where the seasonal variation and the error dependence on the temporal baseline are analyzed. Estimated velocities were compared with reference velocities derived from DLR’s TanDEM-X SAR/InSAR data over the fast-moving glacier outlet, where Sentinel-1 results agree within 4% compared to 3–7% for Landsat-8. A comprehensive apples-to-apples comparison is made with regard to runtime and accuracy between multiple implementations of the proposed routine and the widely-used “dense ampcor" program from NASA/JPL’s ISCE software. autoRIFT is shown to provide two orders of magnitude of runtime improvement with a 20% improvement in accuracy. Article in Journal/Newspaper Jakobshavn Jakobshavn isbræ Directory of Open Access Journals: DOAJ Articles Jakobshavn Isbræ ENVELOPE(-49.917,-49.917,69.167,69.167) Remote Sensing 13 4 749 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
feature tracking optical radar satellite imagery surface displacement glacier velocity Science Q |
spellingShingle |
feature tracking optical radar satellite imagery surface displacement glacier velocity Science Q Yang Lei Alex Gardner Piyush Agram Autonomous Repeat Image Feature Tracking (autoRIFT) and Its Application for Tracking Ice Displacement |
topic_facet |
feature tracking optical radar satellite imagery surface displacement glacier velocity Science Q |
description |
In this paper, we build on past efforts with regard to the implementation of an efficient feature tracking algorithm for the mass processing of satellite images. This generic open-source feature tracking routine can be applied to any type of imagery to measure sub-pixel displacements between images. The routine consists of a feature tracking module (autoRIFT) that enhances computational efficiency and a geocoding module (Geogrid) that mitigates problems found in existing geocoding algorithms. When applied to satellite imagery, autoRIFT can run on a grid in the native image coordinates (such as radar or map) and, when used in conjunction with the Geogrid module, on a user-defined grid in geographic Cartesian coordinates such as Universal Transverse Mercator or Polar Stereographic. To validate the efficiency and accuracy of this approach, we demonstrate its use for tracking ice motion by using ESA’s Sentinel-1A/B radar data (seven pairs) and NASA’s Landsat-8 optical data (seven pairs) collected over Greenland’s Jakobshavn Isbræ glacier in 2017. Feature-tracked velocity errors are characterized over stable surfaces, where the best Sentinel-1A/B pair with a 6 day separation has errors in X / Y of 12 m/year or 39 m/year, compared to 22 m/year or 31 m/year for Landsat-8 with a 16-day separation. Different error sources for radar and optical image pairs are investigated, where the seasonal variation and the error dependence on the temporal baseline are analyzed. Estimated velocities were compared with reference velocities derived from DLR’s TanDEM-X SAR/InSAR data over the fast-moving glacier outlet, where Sentinel-1 results agree within 4% compared to 3–7% for Landsat-8. A comprehensive apples-to-apples comparison is made with regard to runtime and accuracy between multiple implementations of the proposed routine and the widely-used “dense ampcor" program from NASA/JPL’s ISCE software. autoRIFT is shown to provide two orders of magnitude of runtime improvement with a 20% improvement in accuracy. |
format |
Article in Journal/Newspaper |
author |
Yang Lei Alex Gardner Piyush Agram |
author_facet |
Yang Lei Alex Gardner Piyush Agram |
author_sort |
Yang Lei |
title |
Autonomous Repeat Image Feature Tracking (autoRIFT) and Its Application for Tracking Ice Displacement |
title_short |
Autonomous Repeat Image Feature Tracking (autoRIFT) and Its Application for Tracking Ice Displacement |
title_full |
Autonomous Repeat Image Feature Tracking (autoRIFT) and Its Application for Tracking Ice Displacement |
title_fullStr |
Autonomous Repeat Image Feature Tracking (autoRIFT) and Its Application for Tracking Ice Displacement |
title_full_unstemmed |
Autonomous Repeat Image Feature Tracking (autoRIFT) and Its Application for Tracking Ice Displacement |
title_sort |
autonomous repeat image feature tracking (autorift) and its application for tracking ice displacement |
publisher |
MDPI AG |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13040749 https://doaj.org/article/d677c9b8ceda42e5a67cb4b3dc253908 |
long_lat |
ENVELOPE(-49.917,-49.917,69.167,69.167) |
geographic |
Jakobshavn Isbræ |
geographic_facet |
Jakobshavn Isbræ |
genre |
Jakobshavn Jakobshavn isbræ |
genre_facet |
Jakobshavn Jakobshavn isbræ |
op_source |
Remote Sensing, Vol 13, Iss 4, p 749 (2021) |
op_relation |
https://www.mdpi.com/2072-4292/13/4/749 https://doaj.org/toc/2072-4292 doi:10.3390/rs13040749 2072-4292 https://doaj.org/article/d677c9b8ceda42e5a67cb4b3dc253908 |
op_doi |
https://doi.org/10.3390/rs13040749 |
container_title |
Remote Sensing |
container_volume |
13 |
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4 |
container_start_page |
749 |
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