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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.10456 |
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| _version_ | 1866916250052984832 |
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| author | Patel, Muhammed Chen, Xinwei Xu, Linlin Chen, Yuhao Scott, K Andrea Clausi, David A. |
| author_facet | Patel, Muhammed Chen, Xinwei Xu, Linlin Chen, Yuhao Scott, K Andrea Clausi, David A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_10456 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Region-level labels in ice charts can produce pixel-level segmentation for Sea Ice types Patel, Muhammed Chen, Xinwei Xu, Linlin Chen, Yuhao Scott, K Andrea Clausi, David A. Computer Vision and Pattern Recognition 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. |
| title | Region-level labels in ice charts can produce pixel-level segmentation for Sea Ice types |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.10456 |