Toward Polar Sea-Ice Classification using Color-based Segmentation and Auto-labeling of Sentinel-2 Imagery to Train an Efficient Deep Learning Model ...

• Main Authors: Iqrah, Jurdana Masuma, Koo, Younghyun, Wang, Wei, Xie, Hongjie, Prasad, Sushil
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: arXiv 2023
• Subjects: Computer Vision and Pattern Recognition cs.CV; Machine Learning cs.LG; Image and Video Processing eess.IV; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Antarctic; The Antarctic; Ross Sea; The Sentinel; Antarc*; Sea ice
• Online Access: https://dx.doi.org/10.48550/arxiv.2303.12719; https://arxiv.org/abs/2303.12719

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 ... : 2nd Annual AAAI Workshop on AI to Accelerate Science and Engineering (AI2ASE), February 2023 ...