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Seabed Classification From Multispectral Multibeam Data
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DOI：10.1109/JOE.2023.3267795.png)
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Abstract
En 中文
Given the recent increase in the availability of multispectral multibeam echosounder data, this work aims to identify suitable processing and classification methodologies for seabed classification based on such data. We propose a complete processing and classification pipeline and investigate the adequacy of state-of-the-art classification algorithms to perform seabed classification based on multispectral backscatter data alone, and when additional data sources are considered. Starting from raw acquisition data, we generate region-wide multispectral backscatter composite images through noise removal, inpainting/gap-filling and mosaicking. Ground truth data from in situ seabed samples are used. We have tried different classification methods, including random forests, support vector machines, and multilayer perceptrons, with the latter providing the best results. Quantitative and qualitative evaluation on five surveys indicate high classification performance based only on multispectral backscatter data, while additional features, like bathymetry, bathymetric positional index (BPI), or positional encoding, offer limited gains. We offer a web service for seabed classification from multispectral multibeam echosounder data to further support and increase interest in the topic.
Keywords:
Backscatter
data analysis
echo sounding
machine learning
seabed classification
seafloor mapping
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