Deep Learning-Based Identification of Arctic Ocean Boundaries and Near-Surface Phenomena in Underwater Echograms

Monitoring marine environments is a crucial part of understanding the impact of oceans on global climate and their importance for biodiversity and ecological systems, particularly in the Arctic region. Underwater active acoustic surveys with moored multi-frequency echosounders allow for the continuous collection of valuable data reflecting the complex dynamics of these environments. This paper addresses the automatic identification of sea surface boundaries and near-surface phenomena in echograms using deep learning methods to support researchers such as biologists in their work, who typically rely on time-consuming manual analyses. We propose a two-step process that first characterizes echograms according to the surface conditions using an image classification paradigm and then identifies the sea surface boundary and near-surface bubbles and their extent in the water column using a semantic segmentation paradigm. Segmentation is carried out using surface type-specific models, which perform better than a single global segmentation model. We also propose learning strategies, such as a custom boundary loss function, that further improve performance. Experiments with various image classification and semantic segmentation architectures allow us to select the most efficient models for Arctic echogram analysis that, when used in conjunction within our proposed pipeline and our learning strategies, offer excellent results.