Cristin-resultat-ID: 1952315
Sist endret: 8. november 2021, 12:58
Resultat
Vitenskapelig foredrag
2021

Deep Semi-supervised Target Classification in Multi-frequency Echosounder Data

Bidragsytere:
  • Changkyu Choi

Presentasjon

Navn på arrangementet: Norwegian Society for Image Processing and Machine Learning (NOBIM) Workshop 2021
Sted: Radisson blu hotel, Gardermoen, Oslo, Norway
Dato fra: 13. september 2021
Dato til: 14. september 2021

Arrangør:

Arrangørnavn: Kjersti Engan, Karl Skretting, Jarle Hamar Reksten

Om resultatet

Vitenskapelig foredrag
Publiseringsår: 2021

Beskrivelse Beskrivelse

Tittel

Deep Semi-supervised Target Classification in Multi-frequency Echosounder Data

Sammendrag

Acoustic target classification is a field of research that analyzes the marine acoustic data for the marine ecosystem and fishery management, and within this field, the analysis task of multi-frequency echosounder data is a major interest. The goal is to assign an observed acoustic backscattering intensity to a given acoustic category. The results can be used to estimate the abundance or biomass of the species. One common approach for acoustic target classification is manual categorization, where the operators identify and select regions with similar acoustic properties. However, due to its extremely high cost and the vulnerability to operators' bias, the manual process has limited applicability. Automated and scalable analysis methods are required to cope with the multi-frequency data efficiently. We propose a novel deep semi-supervised learning algorithm for acoustic target classification, which operates when the data is partially annotated. The novelty of our work is that the proposed algorithm exploits the underlying structure of the data including both the annotated part and the unannotated part using two interconnected objective functions, namely a clustering objective and a classification objective. The alternating optimization process of the two objective functions is applied to a single convolutional neural network, allowing the unannotated part of the data to contribute to the decision boundaries with respect to the given classes, which is not applicable for a common supervised deep learning. This method can be applied to the echosounder data as well as being potentially generalized to other data sources since it incorporates the generic idea of deep clustering into supervised deep learning. Extensive experiments conducted on the echosounder data validate the robustness of the proposed method for the patch-based classification and semantic segmentation tasks. The multi-frequency echosounder data used in this study have been annually collected at the North Sea by the Norwegian Institute of Marine Research for the case study of classifying lesser sandeel Ammodytes marinus, a small fish without a swim bladder. The contributions of this work are (1) to develop a {novel} semi-supervised deep learning algorithm that is suitable for segmenting and classifying echosounder data without prior information, and (2) to demonstrate the proposed algorithm on a real test case.

Bidragsytere

Changkyu Choi

  • Tilknyttet:
    Forfatter
    ved Institutt for fysikk og teknologi ved UiT Norges arktiske universitet
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