Senjaliya, Femina Bharatkumar2024-04-292024-04-292024https://hdl.handle.net/1828/16439This 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 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 comprehensive 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.enAvailable to the World Wide WebComputer VisionDeep LearningUnderwater Environment MonitoringEchogramsImage ClassificationImage SegmentationThesis