  Sea ice, crucial to the Arctic and Earth's climate, requires consistent
monitoring and high-resolution mapping. Manual sea ice mapping, however, is
time-consuming and subjective, prompting the need for automated deep
learning-based classification approaches. However, training these algorithms is
challenging because expert-generated ice charts, commonly used as training
data, do not map single ice types but instead map polygons with multiple ice
types. Moreover, the distribution of various ice types in these charts is
frequently imbalanced, resulting in a performance bias towards the dominant
class. In this paper, we present a novel GeoAI approach to training sea ice
classification by formalizing it as a partial label learning task with explicit
confidence scores to address multiple labels and class imbalance. We treat the
polygon-level labels as candidate partial labels, assign the corresponding ice
concentrations as confidence scores to each candidate label, and integrate them
with focal loss to train a Convolutional Neural Network (CNN). Our proposed
approach leads to enhanced performance for sea ice classification in Sentinel-1
dual-polarized SAR images, improving classification accuracy (from 87% to 92%)
and weighted average F-1 score (from 90% to 93%) compared to the conventional
training approach of using one-hot encoded labels and Categorical Cross-Entropy
loss. It also improves the F-1 score in 4 out of the 6 sea ice classes.
