Acoustic target classification in multi-frequency echosounder data is a major interest for the marine ecosystem and fishery management since it can potentially estimate the abundance or biomass of the species. A key problem of current methods is the heavy dependence on the manual categorization of data samples. As a solution, we propose a novel semi-supervised deep learning method leveraging a few annotated data samples together with vast amounts of unannotated data samples, all in a single model. Specifically, two inter-connected objectives, namely, a clustering objective and a classification objective, optimize one shared convolutional neural network in an alternating manner. The clustering objective exploits the underlying structure of all data, both annotated and unannotated; the classification objective enforces a certain consistency to given classes using the few annotated data samples. We evaluate our classification method using echosounder data from the sandeel case study in the North Sea. In the semi-supervised setting with only a tenth of the training data annotated, our method achieves 67.6% accuracy, outperforming a conventional semi-supervised method by 7.0 percentage points. When applying the proposed method in a fully supervised setup, we achieve 74.7% accuracy, surpassing the standard supervised deep learning method by 4.7 percentage points.

Multifrequency echosounder data can provide a broad understanding of the underwater environment in a noninvasive manner. The analysis of echosounder data is, hence, a topic of great importance for the marine ecosystem. Semantic segmentation, a deep learning-based analysis method predicting the class attribute of each acoustic intensity, has recently been in the spotlight of the fisheries and aquatic industry since its result can be used to estimate the abundance of marine organisms. However, a fundamental problem with current methods is the massive reliance on the availability of large amounts of annotated training data, which can only be acquired through expensive handcrafted annotation processes, making such approaches unrealistic in practice. As a solution to this challenge, we propose a novel approach, where we leverage a small amount of annotated data (supervised deep learning) and a large amount of readily available unannotated data (unsupervised learning), yielding a new data-efficient and accurate semisupervised semantic segmentation method, all embodied into a single end-to-end trainable convolutional neural network architecture. Our method is evaluated on representative data from a sandeel survey in the North Sea conducted by the Norwegian Institute of Marine Research. The rigorous experiments validate that our method achieves comparable results utilizing only 40% of the annotated data on which the supervised method is trained, by leveraging unannotated data.

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