A Two-Round Weight Voting Strategy-Based Ensemble Learning Method for Sea Ice Classification of Sentinel-1 Imagery
Sea ice information in the Arctic region is essential for climatic change monitoring and ship navigation. Although many sea ice classification methods have been put forward, the accuracy and usability of classification systems can still be improved. In this paper, a two-round weight voting strategy-...
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MDPI AG
2021-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/19/3945 |
| _version_ | 1797515812253204480 |
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| author | Bin Wang Linghui Xia Dongmei Song Zhongwei Li Ning Wang |
| author_facet | Bin Wang Linghui Xia Dongmei Song Zhongwei Li Ning Wang |
| author_sort | Bin Wang |
| collection | DOAJ |
| description | Sea ice information in the Arctic region is essential for climatic change monitoring and ship navigation. Although many sea ice classification methods have been put forward, the accuracy and usability of classification systems can still be improved. In this paper, a two-round weight voting strategy-based ensemble learning method is proposed for refining sea ice classification. The proposed method includes three main steps. (1) The preferable features of sea ice are constituted by polarization features (HH, HV, HH/HV) and the top six GLCM-derived texture features via a random forest. (2) The initial classification maps can then be generated by an ensemble learning method, which includes six base classifiers (NB, DT, KNN, LR, ANN, and SVM). The tuned voting weights by a genetic algorithm are employed to obtain the category score matrix and, further, the first coarse classification result. (3) Some pixels may be misclassified due to their corresponding numerically close score value. By introducing an experiential score threshold, each pixel is identified as a fuzzy or an explicit pixel. The fuzzy pixels can then be further rectified based on the local similarity of the neighboring explicit pixels, thereby yielding the final precise classification result. The proposed method was examined on 18 Sentinel-1 EW images, which were captured in the Northeast Passage from November 2019 to April 2020. The experiments show that the proposed method can effectively maintain the edge profile of sea ice and restrain noise from SAR. It is superior to the current mainstream ensemble learning algorithms with the overall accuracy reaching 97%. The main contribution of this study is proposing a superior weight voting strategy in the ensemble learning method for sea ice classification of Sentinel-1 imagery, which is of great significance for guiding secure ship navigation and ice hazard forecasting in winter. |
| first_indexed | 2024-03-10T06:52:32Z |
| format | Article |
| id | doaj.art-dddef8ba46a244a893f5e5081431f366 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T06:52:32Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-dddef8ba46a244a893f5e5081431f3662023-11-22T16:43:10ZengMDPI AGRemote Sensing2072-42922021-10-011319394510.3390/rs13193945A Two-Round Weight Voting Strategy-Based Ensemble Learning Method for Sea Ice Classification of Sentinel-1 ImageryBin Wang0Linghui Xia1Dongmei Song2Zhongwei Li3Ning Wang4College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, ChinaCollege of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, ChinaCollege of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, ChinaCollege of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, ChinaThe North China Sea Marine Forecasting Center, State Oceanic Administration, Qingdao 266061, ChinaSea ice information in the Arctic region is essential for climatic change monitoring and ship navigation. Although many sea ice classification methods have been put forward, the accuracy and usability of classification systems can still be improved. In this paper, a two-round weight voting strategy-based ensemble learning method is proposed for refining sea ice classification. The proposed method includes three main steps. (1) The preferable features of sea ice are constituted by polarization features (HH, HV, HH/HV) and the top six GLCM-derived texture features via a random forest. (2) The initial classification maps can then be generated by an ensemble learning method, which includes six base classifiers (NB, DT, KNN, LR, ANN, and SVM). The tuned voting weights by a genetic algorithm are employed to obtain the category score matrix and, further, the first coarse classification result. (3) Some pixels may be misclassified due to their corresponding numerically close score value. By introducing an experiential score threshold, each pixel is identified as a fuzzy or an explicit pixel. The fuzzy pixels can then be further rectified based on the local similarity of the neighboring explicit pixels, thereby yielding the final precise classification result. The proposed method was examined on 18 Sentinel-1 EW images, which were captured in the Northeast Passage from November 2019 to April 2020. The experiments show that the proposed method can effectively maintain the edge profile of sea ice and restrain noise from SAR. It is superior to the current mainstream ensemble learning algorithms with the overall accuracy reaching 97%. The main contribution of this study is proposing a superior weight voting strategy in the ensemble learning method for sea ice classification of Sentinel-1 imagery, which is of great significance for guiding secure ship navigation and ice hazard forecasting in winter.https://www.mdpi.com/2072-4292/13/19/3945sea ice classificationensemble learningweight votingSentinel-1 imagery |
| spellingShingle | Bin Wang Linghui Xia Dongmei Song Zhongwei Li Ning Wang A Two-Round Weight Voting Strategy-Based Ensemble Learning Method for Sea Ice Classification of Sentinel-1 Imagery Remote Sensing sea ice classification ensemble learning weight voting Sentinel-1 imagery |
| title | A Two-Round Weight Voting Strategy-Based Ensemble Learning Method for Sea Ice Classification of Sentinel-1 Imagery |
| title_full | A Two-Round Weight Voting Strategy-Based Ensemble Learning Method for Sea Ice Classification of Sentinel-1 Imagery |
| title_fullStr | A Two-Round Weight Voting Strategy-Based Ensemble Learning Method for Sea Ice Classification of Sentinel-1 Imagery |
| title_full_unstemmed | A Two-Round Weight Voting Strategy-Based Ensemble Learning Method for Sea Ice Classification of Sentinel-1 Imagery |
| title_short | A Two-Round Weight Voting Strategy-Based Ensemble Learning Method for Sea Ice Classification of Sentinel-1 Imagery |
| title_sort | two round weight voting strategy based ensemble learning method for sea ice classification of sentinel 1 imagery |
| topic | sea ice classification ensemble learning weight voting Sentinel-1 imagery |
| url | https://www.mdpi.com/2072-4292/13/19/3945 |
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