A Scalable, Supervised Classification of Seabed Sediment Waves Using an Object-Based Image Analysis Approach
National mapping programs (e.g., INFOMAR and MAREANO) and global efforts (Seabed 2030) acquire large volumes of multibeam echosounder data to map large areas of the seafloor. Developing an objective, automated and repeatable approach to extract meaningful information from such vast quantities of dat...
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ftmdpi:oai:mdpi.com:/2072-4292/13/12/2317/ 2023-08-20T04:10:15+02:00 A Scalable, Supervised Classification of Seabed Sediment Waves Using an Object-Based Image Analysis Approach Gerard Summers Aaron Lim Andrew J. Wheeler agris 2021-06-13 application/pdf https://doi.org/10.3390/rs13122317 EN eng Multidisciplinary Digital Publishing Institute Remote Sensing Image Processing https://dx.doi.org/10.3390/rs13122317 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 13; Issue 12; Pages: 2317 object-based image analysis (OBIA) seafloor classification bathymetric derivatives multibeam echosounder spatial resolution Text 2021 ftmdpi https://doi.org/10.3390/rs13122317 2023-08-01T01:56:43Z National mapping programs (e.g., INFOMAR and MAREANO) and global efforts (Seabed 2030) acquire large volumes of multibeam echosounder data to map large areas of the seafloor. Developing an objective, automated and repeatable approach to extract meaningful information from such vast quantities of data is now essential. Many automated or semi-automated approaches have been defined to achieve this goal. However, such efforts have resulted in classification schemes that are isolated or bespoke, and therefore it is necessary to form a standardised classification method. Sediment wave fields are the ideal platform for this as they maintain consistent morphologies across various spatial scales and influence the distribution of biological assemblages. Here, we apply an object-based image analysis (OBIA) workflow to multibeam bathymetry to compare the accuracy of four classifiers (two multilayer perceptrons, support vector machine, and voting ensemble) in identifying seabed sediment waves across three separate study sites. The classifiers are trained on high-spatial-resolution (0.5 m) multibeam bathymetric data from Cork Harbour, Ireland and are then applied to lower-spatial-resolution EMODnet data (25 m) from the Hemptons Turbot Bank SAC and offshore of County Wexford, Ireland. A stratified 10-fold cross-validation was enacted to assess overfitting to the sample data. Samples were taken from the lower-resolution sites and examined separately to determine the efficacy of classification. Results showed that the voting ensemble classifier achieved the most consistent accuracy scores across the high-resolution and low-resolution sites. This is the first object-based image analysis classification of bathymetric data able to cope with significant disparity in spatial resolution. Applications for this approach include benthic current speed assessments, a geomorphological classification framework for benthic biota, and a baseline for monitoring of marine protected areas. Text Turbot MDPI Open Access Publishing Remote Sensing 13 12 2317 |
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Open Polar |
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MDPI Open Access Publishing |
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ftmdpi |
language |
English |
topic |
object-based image analysis (OBIA) seafloor classification bathymetric derivatives multibeam echosounder spatial resolution |
spellingShingle |
object-based image analysis (OBIA) seafloor classification bathymetric derivatives multibeam echosounder spatial resolution Gerard Summers Aaron Lim Andrew J. Wheeler A Scalable, Supervised Classification of Seabed Sediment Waves Using an Object-Based Image Analysis Approach |
topic_facet |
object-based image analysis (OBIA) seafloor classification bathymetric derivatives multibeam echosounder spatial resolution |
description |
National mapping programs (e.g., INFOMAR and MAREANO) and global efforts (Seabed 2030) acquire large volumes of multibeam echosounder data to map large areas of the seafloor. Developing an objective, automated and repeatable approach to extract meaningful information from such vast quantities of data is now essential. Many automated or semi-automated approaches have been defined to achieve this goal. However, such efforts have resulted in classification schemes that are isolated or bespoke, and therefore it is necessary to form a standardised classification method. Sediment wave fields are the ideal platform for this as they maintain consistent morphologies across various spatial scales and influence the distribution of biological assemblages. Here, we apply an object-based image analysis (OBIA) workflow to multibeam bathymetry to compare the accuracy of four classifiers (two multilayer perceptrons, support vector machine, and voting ensemble) in identifying seabed sediment waves across three separate study sites. The classifiers are trained on high-spatial-resolution (0.5 m) multibeam bathymetric data from Cork Harbour, Ireland and are then applied to lower-spatial-resolution EMODnet data (25 m) from the Hemptons Turbot Bank SAC and offshore of County Wexford, Ireland. A stratified 10-fold cross-validation was enacted to assess overfitting to the sample data. Samples were taken from the lower-resolution sites and examined separately to determine the efficacy of classification. Results showed that the voting ensemble classifier achieved the most consistent accuracy scores across the high-resolution and low-resolution sites. This is the first object-based image analysis classification of bathymetric data able to cope with significant disparity in spatial resolution. Applications for this approach include benthic current speed assessments, a geomorphological classification framework for benthic biota, and a baseline for monitoring of marine protected areas. |
format |
Text |
author |
Gerard Summers Aaron Lim Andrew J. Wheeler |
author_facet |
Gerard Summers Aaron Lim Andrew J. Wheeler |
author_sort |
Gerard Summers |
title |
A Scalable, Supervised Classification of Seabed Sediment Waves Using an Object-Based Image Analysis Approach |
title_short |
A Scalable, Supervised Classification of Seabed Sediment Waves Using an Object-Based Image Analysis Approach |
title_full |
A Scalable, Supervised Classification of Seabed Sediment Waves Using an Object-Based Image Analysis Approach |
title_fullStr |
A Scalable, Supervised Classification of Seabed Sediment Waves Using an Object-Based Image Analysis Approach |
title_full_unstemmed |
A Scalable, Supervised Classification of Seabed Sediment Waves Using an Object-Based Image Analysis Approach |
title_sort |
scalable, supervised classification of seabed sediment waves using an object-based image analysis approach |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13122317 |
op_coverage |
agris |
genre |
Turbot |
genre_facet |
Turbot |
op_source |
Remote Sensing; Volume 13; Issue 12; Pages: 2317 |
op_relation |
Remote Sensing Image Processing https://dx.doi.org/10.3390/rs13122317 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs13122317 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
12 |
container_start_page |
2317 |
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