3D MAPPING OF BENTHIC HABITAT USING XGBOOST AND STRUCTURE FROM MOTION PHOTOGRAMMETRY
Benthic habitats mapping is essential to the management and conservation of marine ecosystems. The traditional methods of mapping benthic habitats, which involve multibeam data acquisition and manually collecting and annotating imagery data, are time-consuming. However, with technological advances,...
Published in: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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Format: | Article in Journal/Newspaper |
Language: | English |
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Copernicus Publications
2023
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Online Access: | https://doi.org/10.5194/isprs-annals-X-1-W1-2023-1131-2023 https://doaj.org/article/84b9d9ec72f64119a70f2c5a2c29a227 |
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ftdoajarticles:oai:doaj.org/article:84b9d9ec72f64119a70f2c5a2c29a227 2024-01-07T09:45:13+01:00 3D MAPPING OF BENTHIC HABITAT USING XGBOOST AND STRUCTURE FROM MOTION PHOTOGRAMMETRY S. Morsy A. B. Yánez Suárez K. Robert 2023-12-01T00:00:00Z https://doi.org/10.5194/isprs-annals-X-1-W1-2023-1131-2023 https://doaj.org/article/84b9d9ec72f64119a70f2c5a2c29a227 EN eng Copernicus Publications https://isprs-annals.copernicus.org/articles/X-1-W1-2023/1131/2023/isprs-annals-X-1-W1-2023-1131-2023.pdf https://doaj.org/toc/2194-9042 https://doaj.org/toc/2194-9050 doi:10.5194/isprs-annals-X-1-W1-2023-1131-2023 2194-9042 2194-9050 https://doaj.org/article/84b9d9ec72f64119a70f2c5a2c29a227 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol X-1-W1-2023, Pp 1131-1136 (2023) Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820 article 2023 ftdoajarticles https://doi.org/10.5194/isprs-annals-X-1-W1-2023-1131-2023 2023-12-10T01:40:32Z Benthic habitats mapping is essential to the management and conservation of marine ecosystems. The traditional methods of mapping benthic habitats, which involve multibeam data acquisition and manually collecting and annotating imagery data, are time-consuming. However, with technological advances, using machine learning (ML) algorithms with structure-from-motion (SfM) photogrammetry has become a promising approach for mapping benthic habitats accurately and at very high resolutions. This paper explores using SfM photogrammetry and extreme gradient boosting (XGBoost) classifier for benthic habitat 3D mapping of a vertical wall at the Charlie-Gibbs Fracture Zone in the North Atlantic Ocean. The classification workflow started with extracting frames from video footage. The SfM was then applied to reconstruct the 3D point cloud of the wall. Thereafter, nine geometric features were derived from the 3D point cloud geometry. The XGBoost classifier was then used to classify the vertical wall into rock, sponges, and corals (Case 1 - three classes). In addition, we separated the sponges class into three types of sponges: Demospongiae, Hexactinellida , and other Porifera (Case 2 - five classes). Moreover, we compared the results from XGBoost with the widely used ML classifier, random forest (RF). For Case 2, XGBoost achieved an overall accuracy (OA) of 74.45%, while RF achieved 73.10%. The OA improved by about 10% from both classifiers when the three types of sponges were combined into one class (Case 1). Results showed that the presented 3D mapping of benthic habitat has the potential to provide more detailed and accurate information about marine ecosystems. Article in Journal/Newspaper North Atlantic Directory of Open Access Journals: DOAJ Articles ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-1/W1-2023 1131 1136 |
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Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820 |
spellingShingle |
Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820 S. Morsy A. B. Yánez Suárez K. Robert 3D MAPPING OF BENTHIC HABITAT USING XGBOOST AND STRUCTURE FROM MOTION PHOTOGRAMMETRY |
topic_facet |
Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820 |
description |
Benthic habitats mapping is essential to the management and conservation of marine ecosystems. The traditional methods of mapping benthic habitats, which involve multibeam data acquisition and manually collecting and annotating imagery data, are time-consuming. However, with technological advances, using machine learning (ML) algorithms with structure-from-motion (SfM) photogrammetry has become a promising approach for mapping benthic habitats accurately and at very high resolutions. This paper explores using SfM photogrammetry and extreme gradient boosting (XGBoost) classifier for benthic habitat 3D mapping of a vertical wall at the Charlie-Gibbs Fracture Zone in the North Atlantic Ocean. The classification workflow started with extracting frames from video footage. The SfM was then applied to reconstruct the 3D point cloud of the wall. Thereafter, nine geometric features were derived from the 3D point cloud geometry. The XGBoost classifier was then used to classify the vertical wall into rock, sponges, and corals (Case 1 - three classes). In addition, we separated the sponges class into three types of sponges: Demospongiae, Hexactinellida , and other Porifera (Case 2 - five classes). Moreover, we compared the results from XGBoost with the widely used ML classifier, random forest (RF). For Case 2, XGBoost achieved an overall accuracy (OA) of 74.45%, while RF achieved 73.10%. The OA improved by about 10% from both classifiers when the three types of sponges were combined into one class (Case 1). Results showed that the presented 3D mapping of benthic habitat has the potential to provide more detailed and accurate information about marine ecosystems. |
format |
Article in Journal/Newspaper |
author |
S. Morsy A. B. Yánez Suárez K. Robert |
author_facet |
S. Morsy A. B. Yánez Suárez K. Robert |
author_sort |
S. Morsy |
title |
3D MAPPING OF BENTHIC HABITAT USING XGBOOST AND STRUCTURE FROM MOTION PHOTOGRAMMETRY |
title_short |
3D MAPPING OF BENTHIC HABITAT USING XGBOOST AND STRUCTURE FROM MOTION PHOTOGRAMMETRY |
title_full |
3D MAPPING OF BENTHIC HABITAT USING XGBOOST AND STRUCTURE FROM MOTION PHOTOGRAMMETRY |
title_fullStr |
3D MAPPING OF BENTHIC HABITAT USING XGBOOST AND STRUCTURE FROM MOTION PHOTOGRAMMETRY |
title_full_unstemmed |
3D MAPPING OF BENTHIC HABITAT USING XGBOOST AND STRUCTURE FROM MOTION PHOTOGRAMMETRY |
title_sort |
3d mapping of benthic habitat using xgboost and structure from motion photogrammetry |
publisher |
Copernicus Publications |
publishDate |
2023 |
url |
https://doi.org/10.5194/isprs-annals-X-1-W1-2023-1131-2023 https://doaj.org/article/84b9d9ec72f64119a70f2c5a2c29a227 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol X-1-W1-2023, Pp 1131-1136 (2023) |
op_relation |
https://isprs-annals.copernicus.org/articles/X-1-W1-2023/1131/2023/isprs-annals-X-1-W1-2023-1131-2023.pdf https://doaj.org/toc/2194-9042 https://doaj.org/toc/2194-9050 doi:10.5194/isprs-annals-X-1-W1-2023-1131-2023 2194-9042 2194-9050 https://doaj.org/article/84b9d9ec72f64119a70f2c5a2c29a227 |
op_doi |
https://doi.org/10.5194/isprs-annals-X-1-W1-2023-1131-2023 |
container_title |
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
container_volume |
X-1/W1-2023 |
container_start_page |
1131 |
op_container_end_page |
1136 |
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1787426705059610624 |