Matching Vector Filtering Methods For Sea Ice Motion Detection Using SAR Imagery Feature Tracking
Applying feature tracking techniques to synthetic aperture radar (SAR) imagery generates high-resolution sea ice motion fields. However, the bad matching vectors still exist after the Nearest Neighbor Distance Ratio test and contaminate the derived motion fields, which need to be identified and filt...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9849040/ |
| _version_ | 1798042870121234432 |
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| author | Chaoyue Li Gang Li Zhuoqi Chen Xue Wang Xiao Cheng |
| author_facet | Chaoyue Li Gang Li Zhuoqi Chen Xue Wang Xiao Cheng |
| author_sort | Chaoyue Li |
| collection | DOAJ |
| description | Applying feature tracking techniques to synthetic aperture radar (SAR) imagery generates high-resolution sea ice motion fields. However, the bad matching vectors still exist after the Nearest Neighbor Distance Ratio test and contaminate the derived motion fields, which need to be identified and filtered out. In this article, we propose two algorithms to eliminate such wrong matching vectors. The first employs the matching results derived by the maximum cross-correlation (MCC) method as the reference motion fields to evaluate such wrong matches. The second method employs the local spatial consistency presumption of sea ice motion fields. A Voronoi diagram is applied to slice the overlapping area of two SAR images into many fractions, and each fraction extends its size 50% outward to calculate the regional mean sea ice flow vector and standard deviation. Any vector within the fraction that exceeds 3 times the regional standard deviation will be recognized as an outlier and filtered out. Two methods are tested to two cases with strong rotation or irregular sea ice motion fields derived from Sentinel-1 imagery. The overall accuracy of our two methods is 93.9% and 98.7%, and they sacrifice 6.12% /1.22% of the correct vectors to filter out 100.0% / 94.12% of the wrong vectors for the MCC referenced filter and Voronoi fragmented filter, respectively. |
| first_indexed | 2024-04-11T22:41:26Z |
| format | Article |
| id | doaj.art-67d0e08737864a2bafc5dfc4d246e6f3 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-11T22:41:26Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-67d0e08737864a2bafc5dfc4d246e6f32022-12-22T03:58:58ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352022-01-01156197620210.1109/JSTARS.2022.31960269849040Matching Vector Filtering Methods For Sea Ice Motion Detection Using SAR Imagery Feature TrackingChaoyue Li0Gang Li1https://orcid.org/0000-0001-5684-2408Zhuoqi Chen2https://orcid.org/0000-0003-1307-4941Xue Wang3https://orcid.org/0000-0002-6639-3092Xiao Cheng4https://orcid.org/0000-0001-6910-6565School of Geospatial Engineering and Science, Sun Yat-sen University, Guangzhou, ChinaSchool of Geospatial Engineering and Science, Sun Yat-sen University, Guangzhou, ChinaSchool of Geospatial Engineering and Science, Sun Yat-sen University, Guangzhou, ChinaSchool of Geospatial Engineering and Science, Sun Yat-sen University, Guangzhou, ChinaSchool of Geospatial Engineering and Science, Sun Yat-sen University, Guangzhou, ChinaApplying feature tracking techniques to synthetic aperture radar (SAR) imagery generates high-resolution sea ice motion fields. However, the bad matching vectors still exist after the Nearest Neighbor Distance Ratio test and contaminate the derived motion fields, which need to be identified and filtered out. In this article, we propose two algorithms to eliminate such wrong matching vectors. The first employs the matching results derived by the maximum cross-correlation (MCC) method as the reference motion fields to evaluate such wrong matches. The second method employs the local spatial consistency presumption of sea ice motion fields. A Voronoi diagram is applied to slice the overlapping area of two SAR images into many fractions, and each fraction extends its size 50% outward to calculate the regional mean sea ice flow vector and standard deviation. Any vector within the fraction that exceeds 3 times the regional standard deviation will be recognized as an outlier and filtered out. Two methods are tested to two cases with strong rotation or irregular sea ice motion fields derived from Sentinel-1 imagery. The overall accuracy of our two methods is 93.9% and 98.7%, and they sacrifice 6.12% /1.22% of the correct vectors to filter out 100.0% / 94.12% of the wrong vectors for the MCC referenced filter and Voronoi fragmented filter, respectively.https://ieeexplore.ieee.org/document/9849040/Feature trackingimage matchingsea ice motionsynthetic aperture radar (SAR) |
| spellingShingle | Chaoyue Li Gang Li Zhuoqi Chen Xue Wang Xiao Cheng Matching Vector Filtering Methods For Sea Ice Motion Detection Using SAR Imagery Feature Tracking IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Feature tracking image matching sea ice motion synthetic aperture radar (SAR) |
| title | Matching Vector Filtering Methods For Sea Ice Motion Detection Using SAR Imagery Feature Tracking |
| title_full | Matching Vector Filtering Methods For Sea Ice Motion Detection Using SAR Imagery Feature Tracking |
| title_fullStr | Matching Vector Filtering Methods For Sea Ice Motion Detection Using SAR Imagery Feature Tracking |
| title_full_unstemmed | Matching Vector Filtering Methods For Sea Ice Motion Detection Using SAR Imagery Feature Tracking |
| title_short | Matching Vector Filtering Methods For Sea Ice Motion Detection Using SAR Imagery Feature Tracking |
| title_sort | matching vector filtering methods for sea ice motion detection using sar imagery feature tracking |
| topic | Feature tracking image matching sea ice motion synthetic aperture radar (SAR) |
| url | https://ieeexplore.ieee.org/document/9849040/ |
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