Sea ice characterization with convolutional neural networks
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 com...
| Main Authors: | , , , , |
|---|---|
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
SNAME
2022
|
| Subjects: | |
| 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 |
| Summary: | 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 |
|---|