The work “Seafloor mapping from multispectral multibeam acoustic data at the European Open Science Cloud” has been published with the support of NEANIAS project.
- Mertikas, P [1]. and Karantzalos, K [1][2] (2020).: “Seafloor mapping from multispectral multibeam acoustic data at the European Open Science Cloud”, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 985–990.
- [1] Remote Sensing Laboratory, National Technical University of Athens, Heroon Polytechniou 9, 15780 Zographos, Greece; [2] Research and Innovation Center in Information, Communication and Knowledge Technologies “Athena”, Greece.
Abstract
Recent technological advances in the underwater sensing instrumentation provide currently active multibeam echosounders that can acquire backscatter observations from multiple spectral frequencies. In this paper, the main objective was to design, develop and validate an efficient and robust multispectral, multibeam data processing framework including advanced machine learning tools for seabed classification. In order to do so, we have integrated different machine learning tools like support vector machines and random forests towards the classification of seabed classes. We have performed extensive experiments with different splitting ratios, regarding training and testing sets, in order to assess possible overfitting. The entire pipeline has been implemented in a scalable containerized manner in order to be deployed in cloud infrastructures and more specifically at the European Open Science Cloud. Experimental results, the performed qualitative and quantitative evaluation along with the comparison with the state of the art indicated the quite promising potential of our approach.
Keywords
Seabed, Multiple frequency, Echosounder, Sonar, Classification, Machine Learning, Validation
Acknowledgements
This work has been supported by the NEANIAS project which is funded by the European Union under the Horizon 2020 research and innovation programme via the grant agreement No. 863448.
This contribution has been peer-reviewed.
More information at International Society for Photogrammetry and Remote Sensing.