Region-level labels in ice charts can produce pixel-level segmentation for Sea Ice types

• URL: http://arxiv.org/abs/2405.10456v1
• Date: Thu, 16 May 2024 21:54:33 GMT
• Title: Region-level labels in ice charts can produce pixel-level segmentation for Sea Ice types
• Authors: Muhammed Patel, Xinwei Chen, Linlin Xu, Yuhao Chen, K Andrea Scott, David A. Clausi,
• Abstract summary: We present a weakly supervised learning method for sea ice classification with lower-resolution labels from expert-annotated ice charts. Our method outperforms the fully supervised U-Net benchmark in both mapping resolution and class-wise accuracy.
• Score: 12.480532138980834
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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