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• W3006329892 abstract "There is a great demand to develop new acoustic techniques to efficiently map the seabed and automate the interpretation of acoustic, sedimentological, and imaging data sets, eliminating subjectivity. Here, we evaluate the potential, limitations and complementariety of distinct supervised and automatic classification techniques in the mapping of reefs by comparing these results with a reference map. The study was carried out in the Abrolhos Continental Shelf (Eastern Brazilian Continental Margin) using a multibeam echosounder and side scan sonar (SSS) dataset. Two automatic supervised techniques were applied. A reference map was derived by detailed manual interpretation carried out by three experts. The two supervised classification techniques were: benthic terrain modeler (BTM), a morphometric classification with focus on spatial analyses of the bathymetric grid derivatives, and object-based image analysis (OBIA), a segmentation applied to the backscatter data from the SSS mosaic. Both automatic techniques obtained similar values of reef coverage area, but overestimated the reef area when compared with the reference map. The agreement between BTM and OBIA results and the reference map was 69% and 67%, respectively. Disagreement was mainly due to quantity of reef (both methods over-estimated reef), while the disagreement in spatial allocation was relatively low, it indicates that both methods are reasonable representation of the spatial patterns of reef. Efficient mapping of reef in the wider area of the Abrolhos Continental Shelf will be best achieved by a further development of automatic methods tested against reference maps obained from representative areas of the seabed. By combining the results of the two automatic methods, it was possible to create an ensemble map, which achieved better agreement with the reference dataset." @default.
• W3006329892 created "2020-02-24" @default.
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• W3006329892 date "2020-02-15" @default.
• W3006329892 modified "2023-10-16" @default.
• W3006329892 title "Reef Mapping Using Different Seabed Automatic Classification Tools" @default.
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• W3006329892 doi "https://doi.org/10.3390/geosciences10020072" @default.
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