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...

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Published in:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Main Authors: Bin He, Xi Zhao, Ying Chen, Chuang Liu, Xiaoping Pang
Format: Article in Journal/Newspaper
Language:English
Published: IEEE 2022
Subjects:
Online Access:https://doi.org/10.1109/JSTARS.2022.3178117
https://doaj.org/article/5d1e1c39fcf84fbebf02ee43055f608f
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spelling 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: &#x2212;0.150 cm&#x002F;s, NNSR: 0.407 cm&#x002F;s), lower root mean square error (KVFC: 0.476 cm&#x002F;s, NNSR: 1.817 cm&#x002F;s), and lower angle deviation (KVFC: 3.542&#x00B0;, NNSR: 10.318&#x00B0;) 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
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language 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: &#x2212;0.150 cm&#x002F;s, NNSR: 0.407 cm&#x002F;s), lower root mean square error (KVFC: 0.476 cm&#x002F;s, NNSR: 1.817 cm&#x002F;s), and lower angle deviation (KVFC: 3.542&#x00B0;, NNSR: 10.318&#x00B0;) 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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