Evaluation of Modified Reflection Symmetry Decomposition Polarization Features for Sea Ice Classification
In synthetic aperture radar (SAR) image sea ice classification, the polarization decomposition techniques are used to enhance classification accuracy. However, traditional methods, such as Freeman–Durden (FD) and H/A/α decomposition, struggle to accurately characterize complex scattering mechanisms,...
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MDPI AG
2025-04-01
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| author | Tianlang Lan Chengfei Jiang Xiaofan Luo Wentao An |
| author_facet | Tianlang Lan Chengfei Jiang Xiaofan Luo Wentao An |
| author_sort | Tianlang Lan |
| collection | DOAJ |
| description | In synthetic aperture radar (SAR) image sea ice classification, the polarization decomposition techniques are used to enhance classification accuracy. However, traditional methods, such as Freeman–Durden (FD) and H/A/α decomposition, struggle to accurately characterize complex scattering mechanisms, limiting their ability to differentiate between various sea ice types. This paper proposes using the Modified Reflection Symmetry Decomposition (MRSD) method to extract polarization features from Gaofen-3 (GF-3) satellite fully polarimetric SAR data for sea ice classification tests. The study data included three types of sea surface: open water (OW), young ice (YI), and first-year ice (FYI). In this research, backscattering coefficients were combined with FD, H/A/α, and MRSD polarization features to create eight feature combinations for comparative analysis. Three machine learning algorithms, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machines (SVM), were also used for the comparative analysis. The results show that MRSD polarization features significantly improve model performance, particularly distinguishing among sea ice categories. Compared to using only the backscatter coefficient, MRSD polarization features increased model classification accuracy by approximately 4% to 13%, outperforming FD and H/A/α polarization features. The XGBoost model trained with MRSD polarization features achieves excellent classification results, with classification accuracies of 0.9630, 0.9126, and 0.9451 for OW, YI, and FYI. Additionally, the model achieved a Kappa coefficient of 0.9105 and an F1-score of 0.9403. Feature importance and SHapley Additive exPlanations (SHAP) analysis further demonstrate the physical significance of the MRSD polarization features and their role in model decision-making, suggesting that the scattered component power plays a crucial role in the model’s classification decision. Compared to traditional decomposition methods, MRSD provides a more detailed characterization of scattering mechanisms, offering a comprehensive understanding of the physical properties of sea ice. This paper systematically demonstrates the superior effectiveness of MRSD polarization features for sea ice classification, presenting a new scheme for more accurate classification. |
| format | Article |
| id | doaj-art-42328ff94c3e458b83dea1523a68d007 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-42328ff94c3e458b83dea1523a68d0072025-08-20T03:49:22ZengMDPI AGRemote Sensing2072-42922025-04-01179158410.3390/rs17091584Evaluation of Modified Reflection Symmetry Decomposition Polarization Features for Sea Ice ClassificationTianlang Lan0Chengfei Jiang1Xiaofan Luo2Wentao An3School of Marine Science and Technology, Tianjin University, Tianjin 300072, ChinaNational Satellite Ocean Application Service, Beijing 100081, ChinaSchool of Marine Science and Technology, Tianjin University, Tianjin 300072, ChinaNational Satellite Ocean Application Service, Beijing 100081, ChinaIn synthetic aperture radar (SAR) image sea ice classification, the polarization decomposition techniques are used to enhance classification accuracy. However, traditional methods, such as Freeman–Durden (FD) and H/A/α decomposition, struggle to accurately characterize complex scattering mechanisms, limiting their ability to differentiate between various sea ice types. This paper proposes using the Modified Reflection Symmetry Decomposition (MRSD) method to extract polarization features from Gaofen-3 (GF-3) satellite fully polarimetric SAR data for sea ice classification tests. The study data included three types of sea surface: open water (OW), young ice (YI), and first-year ice (FYI). In this research, backscattering coefficients were combined with FD, H/A/α, and MRSD polarization features to create eight feature combinations for comparative analysis. Three machine learning algorithms, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machines (SVM), were also used for the comparative analysis. The results show that MRSD polarization features significantly improve model performance, particularly distinguishing among sea ice categories. Compared to using only the backscatter coefficient, MRSD polarization features increased model classification accuracy by approximately 4% to 13%, outperforming FD and H/A/α polarization features. The XGBoost model trained with MRSD polarization features achieves excellent classification results, with classification accuracies of 0.9630, 0.9126, and 0.9451 for OW, YI, and FYI. Additionally, the model achieved a Kappa coefficient of 0.9105 and an F1-score of 0.9403. Feature importance and SHapley Additive exPlanations (SHAP) analysis further demonstrate the physical significance of the MRSD polarization features and their role in model decision-making, suggesting that the scattered component power plays a crucial role in the model’s classification decision. Compared to traditional decomposition methods, MRSD provides a more detailed characterization of scattering mechanisms, offering a comprehensive understanding of the physical properties of sea ice. This paper systematically demonstrates the superior effectiveness of MRSD polarization features for sea ice classification, presenting a new scheme for more accurate classification.https://www.mdpi.com/2072-4292/17/9/1584sea ice classificationpolarimetric synthetic aperture radar (PolSAR)polarization decompositionGaofen-3XGBoost |
| spellingShingle | Tianlang Lan Chengfei Jiang Xiaofan Luo Wentao An Evaluation of Modified Reflection Symmetry Decomposition Polarization Features for Sea Ice Classification Remote Sensing sea ice classification polarimetric synthetic aperture radar (PolSAR) polarization decomposition Gaofen-3 XGBoost |
| title | Evaluation of Modified Reflection Symmetry Decomposition Polarization Features for Sea Ice Classification |
| title_full | Evaluation of Modified Reflection Symmetry Decomposition Polarization Features for Sea Ice Classification |
| title_fullStr | Evaluation of Modified Reflection Symmetry Decomposition Polarization Features for Sea Ice Classification |
| title_full_unstemmed | Evaluation of Modified Reflection Symmetry Decomposition Polarization Features for Sea Ice Classification |
| title_short | Evaluation of Modified Reflection Symmetry Decomposition Polarization Features for Sea Ice Classification |
| title_sort | evaluation of modified reflection symmetry decomposition polarization features for sea ice classification |
| topic | sea ice classification polarimetric synthetic aperture radar (PolSAR) polarization decomposition Gaofen-3 XGBoost |
| url | https://www.mdpi.com/2072-4292/17/9/1584 |
| work_keys_str_mv | AT tianlanglan evaluationofmodifiedreflectionsymmetrydecompositionpolarizationfeaturesforseaiceclassification AT chengfeijiang evaluationofmodifiedreflectionsymmetrydecompositionpolarizationfeaturesforseaiceclassification AT xiaofanluo evaluationofmodifiedreflectionsymmetrydecompositionpolarizationfeaturesforseaiceclassification AT wentaoan evaluationofmodifiedreflectionsymmetrydecompositionpolarizationfeaturesforseaiceclassification |