Sea ice surface characterization via semantic segmentation with convolutional neural networks

King, Matthew Peter Ivan (2022) Sea ice surface characterization via semantic segmentation with convolutional neural networks. Masters thesis, Memorial University of Newfoundland.

[img] [English] PDF - Accepted Version
Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission.

Download (11MB)


Visual data 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, along with minor applications of more traditional computer vision techniques. 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. The classes considered in this work are: open water, level ice, broken ice, ridged ice, ice in flexural failure, and an ‘other’ class. Accurate segmentation of these classes is the primary objective of this work. The imagery utilized in this work were captured from two cameras mounted on different piers of the Confederation Bridge during the local ice season. Several hundred of the images collected have been manually labelled and split into training, validation, and testing subsets. These data have been used to train an ensemble of convolutional neural networks. Transfer learning is applied such that the encoder portions of the neural networks have been pretrained on the ImageNet dataset, providing them with the capability to produce meaningful feature maps while significantly reducing the training time required for the overall models to learn the semantic segmentation task at hand.

Item Type: Thesis (Masters)
Item ID: 15765
Additional Information: Includes bibliographical references (pages 104-117)
Keywords: machine learning, semantic segmentation, sea ice, artificial intelligence
Department(s): Engineering and Applied Science, Faculty of
Date: October 2022
Date Type: Submission
Digital Object Identifier (DOI):
Library of Congress Subject Heading: Machine learning; Sea ice; Artificial intelligence; Neural networks (Computer science); Images, Photographic; Image segmentation

Actions (login required)

View Item View Item


Downloads per month over the past year

View more statistics