MYI Floes Identification Based on the Texture and Shape Feature from Dual-Polarized Sentinel-1 Imagery
The Northwest Passage (NWP) in the Arctic is usually covered with hazardous multi-year ice (MYI) and seasonal first-year ice (FYI) in winter, with possible thin ice and open-water areas during transition seasons. Ice classification is important for both marine navigation and climate change studies....
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MDPI AG
2020-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/19/3221 |
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| author | Shiyi Chen Mohammed Shokr Xinqing Li Yufang Ye Zhilun Zhang Fengming Hui Xiao Cheng |
| author_facet | Shiyi Chen Mohammed Shokr Xinqing Li Yufang Ye Zhilun Zhang Fengming Hui Xiao Cheng |
| author_sort | Shiyi Chen |
| collection | DOAJ |
| description | The Northwest Passage (NWP) in the Arctic is usually covered with hazardous multi-year ice (MYI) and seasonal first-year ice (FYI) in winter, with possible thin ice and open-water areas during transition seasons. Ice classification is important for both marine navigation and climate change studies. Satellite-based Synthetic Aperture Radar (SAR) systems have shown advantages of retrieving this information. Operational ice mapping relies on visual analysis of SAR images along with ancillary data. However, these maps estimate ice types and concentrations within large-size polygons of a few tens or hundreds of kilometers, which are subjectively identified and selected by analysts. This study aims at developing an automated algorithm to identify individual MYI floes from SAR images then classify the rest of the image as FYI and other ice types. The algorithm identifies the MYI floes using extended-maximum operator, morphological image processing, and a few geometrical features. Classifying the rest of the image uses texture and neural network model. The input data is a set of Sentinel-1 A/B Extended Wide (EW) mode images, acquired between September and March 2016–2019. Although the overall accuracy (for all type classification) from the new method scored 93.26%, the accuracy from using the texture classifier only was 75.81%. The kappa coefficient from the former was higher than the latter by 0.25. Compared with the operational ice charts from the Canadian Ice Service, ice type maps from the new method show better distribution of MYI at the fine scale of individual floes. Comparison against MYI concentration from two automated algorithms that use a combination of coarse-resolution passive and active microwave data also confirms the advantage of resolving MYI floes from the fine-resolution SAR. |
| first_indexed | 2024-03-10T15:51:45Z |
| format | Article |
| id | doaj.art-6aa9922d96694de6a2f7166df2ecb6c8 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T15:51:45Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-6aa9922d96694de6a2f7166df2ecb6c82023-11-20T15:58:12ZengMDPI AGRemote Sensing2072-42922020-10-011219322110.3390/rs12193221MYI Floes Identification Based on the Texture and Shape Feature from Dual-Polarized Sentinel-1 ImageryShiyi Chen0Mohammed Shokr1Xinqing Li2Yufang Ye3Zhilun Zhang4Fengming Hui5Xiao Cheng6School of Geospatial Engineering and Science, Sun Yat-sen University, Guangzhou 510275, ChinaScience and Technology Branch, Environment and Climate Change Canada, Toronto, ON M3H5T4, CanadaSchool of Geospatial Engineering and Science, Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Geospatial Engineering and Science, Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Geospatial Engineering and Science, Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Geospatial Engineering and Science, Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Geospatial Engineering and Science, Sun Yat-sen University, Guangzhou 510275, ChinaThe Northwest Passage (NWP) in the Arctic is usually covered with hazardous multi-year ice (MYI) and seasonal first-year ice (FYI) in winter, with possible thin ice and open-water areas during transition seasons. Ice classification is important for both marine navigation and climate change studies. Satellite-based Synthetic Aperture Radar (SAR) systems have shown advantages of retrieving this information. Operational ice mapping relies on visual analysis of SAR images along with ancillary data. However, these maps estimate ice types and concentrations within large-size polygons of a few tens or hundreds of kilometers, which are subjectively identified and selected by analysts. This study aims at developing an automated algorithm to identify individual MYI floes from SAR images then classify the rest of the image as FYI and other ice types. The algorithm identifies the MYI floes using extended-maximum operator, morphological image processing, and a few geometrical features. Classifying the rest of the image uses texture and neural network model. The input data is a set of Sentinel-1 A/B Extended Wide (EW) mode images, acquired between September and March 2016–2019. Although the overall accuracy (for all type classification) from the new method scored 93.26%, the accuracy from using the texture classifier only was 75.81%. The kappa coefficient from the former was higher than the latter by 0.25. Compared with the operational ice charts from the Canadian Ice Service, ice type maps from the new method show better distribution of MYI at the fine scale of individual floes. Comparison against MYI concentration from two automated algorithms that use a combination of coarse-resolution passive and active microwave data also confirms the advantage of resolving MYI floes from the fine-resolution SAR.https://www.mdpi.com/2072-4292/12/19/3221sea ice classificationSentinel-1 A/BNorthwest PassageArctic MYI floes |
| spellingShingle | Shiyi Chen Mohammed Shokr Xinqing Li Yufang Ye Zhilun Zhang Fengming Hui Xiao Cheng MYI Floes Identification Based on the Texture and Shape Feature from Dual-Polarized Sentinel-1 Imagery Remote Sensing sea ice classification Sentinel-1 A/B Northwest Passage Arctic MYI floes |
| title | MYI Floes Identification Based on the Texture and Shape Feature from Dual-Polarized Sentinel-1 Imagery |
| title_full | MYI Floes Identification Based on the Texture and Shape Feature from Dual-Polarized Sentinel-1 Imagery |
| title_fullStr | MYI Floes Identification Based on the Texture and Shape Feature from Dual-Polarized Sentinel-1 Imagery |
| title_full_unstemmed | MYI Floes Identification Based on the Texture and Shape Feature from Dual-Polarized Sentinel-1 Imagery |
| title_short | MYI Floes Identification Based on the Texture and Shape Feature from Dual-Polarized Sentinel-1 Imagery |
| title_sort | myi floes identification based on the texture and shape feature from dual polarized sentinel 1 imagery |
| topic | sea ice classification Sentinel-1 A/B Northwest Passage Arctic MYI floes |
| url | https://www.mdpi.com/2072-4292/12/19/3221 |
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