Femina Bharatkumar Senjaliya

Topic

Analyzing Ocean Boundary Phenomena in Echograms: A Deep Learning Approach

Department of Electrical and Computer Engineering

Date & location

• Friday, April 26, 2024

• 10:00 A.M.

• Virtual Defence

Reviewers

Supervisory Committee

• Dr. Alexandra Branzan Albu, Department of Electrical and Computer Engineering, UVic (Supervisor)

• Dr. Tunai Porto Marques, Department of Electrical and Computer Engineering, UVic (Member) 

External Examiner

• Dr. Josh Giles, Department of Mechanical Engineering, University of Victoria 

Chair of Oral Examination

• Dr. Patrick Dunae, Department of History, UVic

   

Abstract

This research puts emphasis on the fundamental part of marine monitoring as an instrument to study how the oceans influence the global climate, biodiversity and ecological systems under the condition of the Arctic region. Utilizing underwater active acoustic surveys conducted with moored multi-frequency echosounders as our source gives us the opportunity to reflect on the complexity of ocean settings. We propose a deep-learning approach to automate the identification of sea surface boundaries and near-surface phenomena in echograms to assist the oceanographers who currently rely heavily on the time-consuming manual analyses. The identification of boundaries at the surface and the occurrence of bubble phenomena are vital to those who investigate marine environments. These factors greatly affect the complex interactions between organisms. We propose a two-step, end-to end, deep learning approach where the first step uses an image classification framework to categorize echograms based on surface conditions and is followed by the second step where we employ semantic segmentation frameworks that help to delineate sea surface and near surface bubbles within the water column. This segmentation in the second step is equipped with a type-specific model that has been proven to outperform a single global segmentation model. Furthermore, our methodology incorporates innovative learning strategies, including a tailored boundary loss function, to enhance model performance. Through com prehensive testing with a range of image classification and semantic segmentation architectures, we identify the most effective models for Arctic echogram analysis. Our proposed deep learning pipeline showcases noteworthy capabilities in accurately characterizing and analyzing marine acoustic data.