Fully supervised deep learning approaches have demonstrated impressive
accuracy in sea ice classification, but their dependence on high-resolution
labels presents a significant challenge due to the difficulty of obtaining such
data. In response, our weakly supervised learning method provides a compelling
alternative by utilizing lower-resolution regional labels from expert-annotated
ice charts. This approach achieves exceptional pixel-level classification
performance by introducing regional loss representations during training to
measure the disparity between predicted and ice chart-derived sea ice type
distributions. Leveraging the AI4Arctic Sea Ice Challenge Dataset, our method
outperforms the fully supervised U-Net benchmark, the top solution of the
AutoIce challenge, in both mapping resolution and class-wise accuracy, marking
a significant advancement in automated operational sea ice mapping.

利用低分辨率区域标签，我们的弱监督学习方法在海冰分类中取得了优异的像素级分类性能，通过在训练中引入区域损失表示来衡量预测结果和冰图导出的海冰类型分布之间的差异。借助 AI4Arctic 海冰挑战数据集，我们的方法在映射分辨率和类别精度方面超过了完全监督的 U-Net 基准和 AutoIce 挑战的最佳解决方案，标志着自动化操作的海冰制图方面的重大进展。