An Object-Based Image Analysis Approach Using Bathymetry and Bathymetric Derivatives to Classify the Seafloor
In this paper, object-based image analysis classification methods are developed that do not rely on backscatter in order to classify the seafloor. Instead, these methods make use of bathymetry, bathymetric derivatives, and grab samples for classification. The classification is performed on image obj...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Geosciences |
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| Online Access: | https://www.mdpi.com/2076-3263/11/2/45 |
| _version_ | 1797408476618555392 |
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| author | Leo Koop Mirjam Snellen Dick G. Simons |
| author_facet | Leo Koop Mirjam Snellen Dick G. Simons |
| author_sort | Leo Koop |
| collection | DOAJ |
| description | In this paper, object-based image analysis classification methods are developed that do not rely on backscatter in order to classify the seafloor. Instead, these methods make use of bathymetry, bathymetric derivatives, and grab samples for classification. The classification is performed on image object statistics. One of the methods utilizes only texture-based features, that is, features that are related to the spatial arrangement of image characteristics. The second method is similar, but relies on a wider set of image object features. The methods were developed and tested using a dataset from Norwegian waters, specifically the Røstbanken area off the coast of Lofoten. The classification results were compared to backscatter-based classification and to grab sample ground-reference data. The algorithm that performed the best was then also applied to a dataset from the Borkumer Stones area close to the island of Schiermonnikoog in Dutch waters. This allowed testing the applicability of the algorithm for different datasets. Because the algorithms that were developed do not require backscatter, the availability of which is much more scarce than bathymetry, and because of the low computational requirements, they could be applied to any area where high-resolution bathymetry and grab samples are available. |
| first_indexed | 2024-03-09T04:00:01Z |
| format | Article |
| id | doaj.art-f958f2801c2340d38d035dbb9a032f5a |
| institution | Directory Open Access Journal |
| issn | 2076-3263 |
| language | English |
| last_indexed | 2024-03-09T04:00:01Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Geosciences |
| spelling | doaj.art-f958f2801c2340d38d035dbb9a032f5a2023-12-03T14:16:13ZengMDPI AGGeosciences2076-32632021-01-011124510.3390/geosciences11020045An Object-Based Image Analysis Approach Using Bathymetry and Bathymetric Derivatives to Classify the SeafloorLeo Koop0Mirjam Snellen1Dick G. Simons2Acoustics Group, Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS Delft, The NetherlandsAcoustics Group, Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS Delft, The NetherlandsAcoustics Group, Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS Delft, The NetherlandsIn this paper, object-based image analysis classification methods are developed that do not rely on backscatter in order to classify the seafloor. Instead, these methods make use of bathymetry, bathymetric derivatives, and grab samples for classification. The classification is performed on image object statistics. One of the methods utilizes only texture-based features, that is, features that are related to the spatial arrangement of image characteristics. The second method is similar, but relies on a wider set of image object features. The methods were developed and tested using a dataset from Norwegian waters, specifically the Røstbanken area off the coast of Lofoten. The classification results were compared to backscatter-based classification and to grab sample ground-reference data. The algorithm that performed the best was then also applied to a dataset from the Borkumer Stones area close to the island of Schiermonnikoog in Dutch waters. This allowed testing the applicability of the algorithm for different datasets. Because the algorithms that were developed do not require backscatter, the availability of which is much more scarce than bathymetry, and because of the low computational requirements, they could be applied to any area where high-resolution bathymetry and grab samples are available.https://www.mdpi.com/2076-3263/11/2/45object-based image analysisseafloor classificationbathymetrybathymetric derrivativesgrab samplesmultibeam echosounder |
| spellingShingle | Leo Koop Mirjam Snellen Dick G. Simons An Object-Based Image Analysis Approach Using Bathymetry and Bathymetric Derivatives to Classify the Seafloor Geosciences object-based image analysis seafloor classification bathymetry bathymetric derrivatives grab samples multibeam echosounder |
| title | An Object-Based Image Analysis Approach Using Bathymetry and Bathymetric Derivatives to Classify the Seafloor |
| title_full | An Object-Based Image Analysis Approach Using Bathymetry and Bathymetric Derivatives to Classify the Seafloor |
| title_fullStr | An Object-Based Image Analysis Approach Using Bathymetry and Bathymetric Derivatives to Classify the Seafloor |
| title_full_unstemmed | An Object-Based Image Analysis Approach Using Bathymetry and Bathymetric Derivatives to Classify the Seafloor |
| title_short | An Object-Based Image Analysis Approach Using Bathymetry and Bathymetric Derivatives to Classify the Seafloor |
| title_sort | object based image analysis approach using bathymetry and bathymetric derivatives to classify the seafloor |
| topic | object-based image analysis seafloor classification bathymetry bathymetric derrivatives grab samples multibeam echosounder |
| url | https://www.mdpi.com/2076-3263/11/2/45 |
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