Sea ice characterization with convolutional neural networks

• Main Authors: King, Matthew, Lamontagne, Philippe, Poirier, Louis, Taylor, Rocky, Briggs, Robert
• Format: Article in Journal/Newspaper
• Language: English
• Published: SNAME 2022
• Subjects: sea ice; maritime safety; machine learning; AI; computer vision; industry trends and new technologies; Sea ice
• Online Access: https://nrc-publications.canada.ca/eng/view/object/?id=bf5c5d0e-d6f0-47dd-b5f2-e56c98479af8; https://nrc-publications.canada.ca/fra/voir/objet/?id=bf5c5d0e-d6f0-47dd-b5f2-e56c98479af8

Visual data is abundantly available and provides rich information about real-world objects. Computer vision is a substantial and growing field, which seeks to distill useful information from photographic imagery. The primary focus of this work centers on the application of machine learning based computer vision algorithms in order to produce characterizations of the visible sea ice conditions. The specific task approached herein is known as semantic segmentation; the methodology by which each region of an image, at an individual pixel level, is assigned a classification from a predetermined set of possible classes. Peer reviewed: Yes NRC publication: Yes