Application of a Convolutional Neural Network for the Detection of Sea Ice Leads

• Published in: Remote Sensing
• Main Authors: Jay P. Hoffman, Steven A. Ackerman, Yinghui Liu, Jeffrey R. Key, Iain L. McConnell
• Format: Text
• Language: English
• Published: Multidisciplinary Digital Publishing Institute 2021
• Subjects: leads; sea ice; MODIS; VIIRS; convolutional neural network; U-Net; Arctic; Sea ice
• Online Access: https://doi.org/10.3390/rs13224571

Despite accounting for a small fraction of the surface area in the Arctic, long and narrow sea ice fractures, known as “leads”, play a critical role in the energy flux between the ocean and atmosphere. As the volume of sea ice in the Arctic has declined over the past few decades, it is increasingly important to monitor the corresponding changes in sea ice leads. A novel approach has been developed using artificial intelligence (AI) to detect sea ice leads using satellite thermal infrared window data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS). In this new approach, a particular type of convolutional neural network, a U-Net, replaces a series of conventional image processing tests from our legacy algorithm. Results show the new approach has a high detection accuracy with F1 Scores on the order of 0.7. Compared to the legacy algorithm, the new algorithm shows improvement, with more true positives, fewer false positives, fewer false negatives, and better agreement between satellite instruments.