A Weakly Supervised Learning Approach for Sea Ice Stage of Development Classification From AI4Arctic Sea Ice Challenge Dataset

Abstract

Deep learning (DL)-based fully supervised approaches have demonstrated remarkable performance in sea ice classification, showcasing their potential for highly accurate results. However, their reliance on high-resolution labels poses a formidable challenge, as obtaining such data can be a difficult task. In contrast, our method based on weakly supervised learning excels by operating with lower-resolution polygon labels while still achieving outstanding performance. This approach enables precise pixel-level classification of ice stage of development (SOD) by learning from region-based labels embedded within expert-annotated ice charts. During training, region-based loss functions are introduced to quantify the disparity between predicted tensors describing SOD distributions and label tensors derived from ice charts. We leverage the AI4Arctic Sea Ice Challenge Dataset, comprising over 500 Sentinel-1 synthetic aperture radar (SAR) images, ancillary multisource data, and corresponding ice charts, for model training and evaluation. Visual interpretation and numerical analysis reveal that our weakly supervised method outperforms the fully supervised U-Net benchmark. It yields more accurate SOD predictions, significantly enhancing mapping resolution and class-wise accuracy. This methodology marks a critical step forward in the quest for automated operational sea ice mapping.