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Enhancing sea ice classification on SAR imagery by integrating texture and polarimetric information with a deep learning model 认领 引用
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作者 Lichen GAO 《Journal of Oceanology and Limnology》 SCIE CAS CSCD 2026年第4期1342-1357,共16页
The satellite synthetic aperture radar(SAR)sensor is one of the most critical tools for monitoring Arctic sea ice.Classifying sea ice types based on SAR images has been a research hotspot.Most existing deep-learning-b... The satellite synthetic aperture radar(SAR)sensor is one of the most critical tools for monitoring Arctic sea ice.Classifying sea ice types based on SAR images has been a research hotspot.Most existing deep-learning-based sea ice classification models rely on the polarimetric information of SAR images while ignoring the gray-level co-occurrence matrix(GLCM)feature.This study develops a three-branch U-Net model for classifying sea ice in SAR images.By integrating polarimetric information,GLCM features,and auxiliary data,the model can classify open water(OW),young ice(YIC),first-year ice(FYI),and old ice(OIC).The model is trained and tested on the well-known AI4Arctic sea ice challenge dataset.Experiments on 57 testing SAR images demonstrate that the proposed model achieves an overall classification accuracy of 91.45% and an Intersection over Union(IoU)of 0.8464 for the four-type classification.Ablation experiments were conducted to evaluate the sensitivity of various GLCM features to sea ice classification.The effectiveness of the three-branch input for fusing polarimetric information,GLCM feature,and auxiliary data is validated.Results indicate that incorporating HV_mean significantly enhances classification performance,with an accuracy increase of approximately 0.7% and an improvement in IoU of 0.9%.The three-branch input structure is more effective than the single-branch structure in fusing three types of inputs,resulting in an accuracy increase of 4.7% and an improvement in IoU of 7%.Therefore,the proposed three-branch U-Net model demonstrates stable and reliable capabilities for classifying OW,YIC,FYI,and OIC in SAR images,providing a new approach for Arctic sea ice monitoring. 展开更多
关键词 synthetic aperture radar(SAR)image sea ice classification gray-level co-occurrence matrix(GLCM) U-Net deep learning
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