Region-level labels in ice charts can produce pixel-level segmentation for Sea Ice types
<|reference_start|>Region-level labels in ice charts can produce pixel-level segmentation for Sea Ice types: 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.<|reference_end|>
@article{patel2024region-level,
    title={Region-level labels in ice charts can produce pixel-level segmentation
  for Sea Ice types},
    author={Muhammed Patel, Xinwei Chen, Linlin Xu, Yuhao Chen, K Andrea Scott,
  David A. Clausi},
    journal={arXiv preprint arXiv:2405.10456},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.10456},
    primaryClass={cs.CV}
}