  Global warming is an urgent issue that is generating catastrophic
environmental changes, such as the melting of sea ice and glaciers,
particularly in the polar regions. The melting pattern and retreat of polar sea
ice cover is an essential indicator of global warming. The Sentinel-2 satellite
(S2) captures high-resolution optical imagery over the polar regions. This
research aims at developing a robust and effective system for classifying polar
sea ice as thick or snow-covered, young or thin, or open water using S2 images.
A key challenge is the lack of labeled S2 training data to serve as the ground
truth. We demonstrate a method with high precision to segment and automatically
label the S2 images based on suitably determined color thresholds and employ
these auto-labeled data to train a U-Net machine model (a fully convolutional
neural network), yielding good classification accuracy. Evaluation results over
S2 data from the polar summer season in the Ross Sea region of the Antarctic
show that the U-Net model trained on auto-labeled data has an accuracy of
90.18% over the original S2 images, whereas the U-Net model trained on manually
labeled data has an accuracy of 91.39%. Filtering out the thin clouds and
shadows from the S2 images further improves U-Net's accuracy, respectively, to
98.97% for auto-labeled and 98.40% for manually labeled training datasets.
