Application of Feature Tracking Using K -Nearest-Neighbor Vector Field Consensus in Sea Ice Tracking
A feature-tracking algorithm algorithm utilizing the proposed K -nearest-neighbor vector field consensus (KVFC) to filter outliers is developed to monitor the dynamic changes of sea ice retrieval from synthetic aperture radar (SAR) images. The KVFC is based on vector field consensus and combines wit...
Published in: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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ftdoajarticles:oai:doaj.org/article:5d1e1c39fcf84fbebf02ee43055f608f 2023-05-15T15:40:37+02:00 Application of Feature Tracking Using K -Nearest-Neighbor Vector Field Consensus in Sea Ice Tracking Bin He Xi Zhao Ying Chen Chuang Liu Xiaoping Pang 2022-01-01T00:00:00Z https://doi.org/10.1109/JSTARS.2022.3178117 https://doaj.org/article/5d1e1c39fcf84fbebf02ee43055f608f EN eng IEEE https://ieeexplore.ieee.org/document/9783199/ https://doaj.org/toc/2151-1535 2151-1535 doi:10.1109/JSTARS.2022.3178117 https://doaj.org/article/5d1e1c39fcf84fbebf02ee43055f608f IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 4326-4336 (2022) Correspondences feature <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> $K$ </named-content>-nearest-neighbor vector field consensus (KVFC) sea-ice drift synthetic aperture radar (SAR) Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 article 2022 ftdoajarticles https://doi.org/10.1109/JSTARS.2022.3178117 2022-12-31T02:46:10Z A feature-tracking algorithm algorithm utilizing the proposed K -nearest-neighbor vector field consensus (KVFC) to filter outliers is developed to monitor the dynamic changes of sea ice retrieval from synthetic aperture radar (SAR) images. The KVFC is based on vector field consensus and combines with local neighborhood correspondences to optimize the elimination of outliers while retaining inliers as many as possible. The proposed KVFC was evaluated and compared with several algorithms on three standard datasets and Sentinel-1 image pairs in the Fram Strait and the Beaufort Sea. The KVFC obtained more sea-ice drift vectors than the nearest neighbor similarity ratio (NNSR) with a 0.7 threshold and generated dense distribution of sea-ice drift vectors combined with the HH and HV channels. Using buoy datasets to calculate the sea-ice drift speed and evaluate algorithm performance, the proposed approach yielded a lower mean error (KVFC: −0.150 cm/s, NNSR: 0.407 cm/s), lower root mean square error (KVFC: 0.476 cm/s, NNSR: 1.817 cm/s), and lower angle deviation (KVFC: 3.542°, NNSR: 10.318°) compared to the NNSR. Article in Journal/Newspaper Beaufort Sea Fram Strait Sea ice Directory of Open Access Journals: DOAJ Articles IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15 4326 4336 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
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English |
topic |
Correspondences feature <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> $K$ </named-content>-nearest-neighbor vector field consensus (KVFC) sea-ice drift synthetic aperture radar (SAR) Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 |
spellingShingle |
Correspondences feature <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> $K$ </named-content>-nearest-neighbor vector field consensus (KVFC) sea-ice drift synthetic aperture radar (SAR) Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Bin He Xi Zhao Ying Chen Chuang Liu Xiaoping Pang Application of Feature Tracking Using K -Nearest-Neighbor Vector Field Consensus in Sea Ice Tracking |
topic_facet |
Correspondences feature <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> $K$ </named-content>-nearest-neighbor vector field consensus (KVFC) sea-ice drift synthetic aperture radar (SAR) Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 |
description |
A feature-tracking algorithm algorithm utilizing the proposed K -nearest-neighbor vector field consensus (KVFC) to filter outliers is developed to monitor the dynamic changes of sea ice retrieval from synthetic aperture radar (SAR) images. The KVFC is based on vector field consensus and combines with local neighborhood correspondences to optimize the elimination of outliers while retaining inliers as many as possible. The proposed KVFC was evaluated and compared with several algorithms on three standard datasets and Sentinel-1 image pairs in the Fram Strait and the Beaufort Sea. The KVFC obtained more sea-ice drift vectors than the nearest neighbor similarity ratio (NNSR) with a 0.7 threshold and generated dense distribution of sea-ice drift vectors combined with the HH and HV channels. Using buoy datasets to calculate the sea-ice drift speed and evaluate algorithm performance, the proposed approach yielded a lower mean error (KVFC: −0.150 cm/s, NNSR: 0.407 cm/s), lower root mean square error (KVFC: 0.476 cm/s, NNSR: 1.817 cm/s), and lower angle deviation (KVFC: 3.542°, NNSR: 10.318°) compared to the NNSR. |
format |
Article in Journal/Newspaper |
author |
Bin He Xi Zhao Ying Chen Chuang Liu Xiaoping Pang |
author_facet |
Bin He Xi Zhao Ying Chen Chuang Liu Xiaoping Pang |
author_sort |
Bin He |
title |
Application of Feature Tracking Using K -Nearest-Neighbor Vector Field Consensus in Sea Ice Tracking |
title_short |
Application of Feature Tracking Using K -Nearest-Neighbor Vector Field Consensus in Sea Ice Tracking |
title_full |
Application of Feature Tracking Using K -Nearest-Neighbor Vector Field Consensus in Sea Ice Tracking |
title_fullStr |
Application of Feature Tracking Using K -Nearest-Neighbor Vector Field Consensus in Sea Ice Tracking |
title_full_unstemmed |
Application of Feature Tracking Using K -Nearest-Neighbor Vector Field Consensus in Sea Ice Tracking |
title_sort |
application of feature tracking using k -nearest-neighbor vector field consensus in sea ice tracking |
publisher |
IEEE |
publishDate |
2022 |
url |
https://doi.org/10.1109/JSTARS.2022.3178117 https://doaj.org/article/5d1e1c39fcf84fbebf02ee43055f608f |
genre |
Beaufort Sea Fram Strait Sea ice |
genre_facet |
Beaufort Sea Fram Strait Sea ice |
op_source |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 4326-4336 (2022) |
op_relation |
https://ieeexplore.ieee.org/document/9783199/ https://doaj.org/toc/2151-1535 2151-1535 doi:10.1109/JSTARS.2022.3178117 https://doaj.org/article/5d1e1c39fcf84fbebf02ee43055f608f |
op_doi |
https://doi.org/10.1109/JSTARS.2022.3178117 |
container_title |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
container_volume |
15 |
container_start_page |
4326 |
op_container_end_page |
4336 |
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1766373207723474944 |