Applying a Multi-Method Framework to Analyze the Multispectral Acoustic Response of the Seafloor
Frontiers in Remote Sensing, 2022 Improvements to acoustic seafloor mapping systems have motivated novel marine geological and benthic biological research. Multibeam echosounders (MBES) have become a mainstream tool for acoustic remote sensing of the seabed.
Pedro S. Menandro +3 more
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Geosciences, 2018 Multi-frequency backscatter data collected from multibeam echosounders (MBESs) is increasingly becoming available. The ability to collect data at multiple frequencies at the same time is expected to allow for better discrimination between seabed ...
Timo C. Gaida +5 more
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Analysis of some problems in classification of seabed bottom characteristics using acoustic backscattering intensity [PDF]
E3S Web of Conferences, 2020 The backscattering intensity collected by multi beam sonar system and scanning sonar system can be used to classify seabed bottom characteristics. However, there are many problems that have not been solved in the practical application.
Jintao FENG +4 more
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Estimating the historical distribution, abundance and ecological contribution of Modiolus modiolus in Strangford Lough, Northern Ireland [PDF]
, 2016 Strangford Lough is a large sheltered marine inlet in Northern Ireland. It is also a designated Special Area of Conservation based partially on the presence of an extensive area of Modiolus modiolus (Linnaeus, 1758) biogenic reef.
Moore, Heather +2 more
core +1 more source
Journal of Applied Geospatial Information, 2023 Classification of seabed types from multibeam echosounder data using machine learning techniques has been widely used in recent decades, such as Random Forest (RF), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Nearest Neighbor (NN).
Steven Solikin +4 more
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The value of remote sensing techniques in supporting effective extrapolation across multiple marine spatial scales [PDF]
, 2017 The reporting of ecological phenomena and environmental status routinely required point observations, collected with traditional sampling approaches to be extrapolated to larger reporting scales.
Elliott, Michael, Strong, James Asa
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Sediment Classification of Small-Size Seabed Acoustic Images Using Convolutional Neural Networks
IEEE Access, 2019 Seabed acoustic images are image data mosaics derived from seafloor acoustic backscattering intensity data, which is related to the type of sediment covering the seabed.
Xiaowen Luo +5 more
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Remote Sensing, 2022 This study presents a novel method to identify optically deep water using purely spectral approaches. Optically deep waters, where the seabed is too deep for a bottom reflectance signal to be returned, is uninformative for seabed mapping.
Chengfa Benjamin Lee +2 more
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Classification of Southern Ocean krill and icefish echoes using random forests [PDF]
, 2016 Acknowledgements The authors thank the crews, fishers, and scientists who conducted the various surveys from which data were obtained. This work was supported by the Government of South Georgia and South Sandwich Islands.
Fallon, Niall G. +2 more
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Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness. [PDF]
PLoS ONE, 2016 Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia's marine jurisdiction.
Jin Li, Maggie Tran, Justy Siwabessy
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