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

• Main Authors: Patel, Muhammed, Chen, Xinwei, Xu, Linlin, Chen, Yuhao, Scott, K Andrea, Clausi, David A.
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: arXiv 2024
• Subjects: Computer Vision and Pattern Recognition cs.CV; FOS Computer and information sciences; Sea ice
• Online Access: https://dx.doi.org/10.48550/arxiv.2405.10456; https://arxiv.org/abs/2405.10456

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. ... : Published at ICLR 2024 Machine Learning for Remote Sensing (ML4RS) Workshop ...