Automated Shoreline Segmentation in Satellite Imagery Using USV Measurements
Generating aerial shoreline segmentation masks can be a daunting task, often requiring manual labeling or correction. This is further problematic because neural segmentation models require decent and abundant data for training, requiring even more manpower to automate the process. In this paper, we...
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MDPI AG
2024-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/23/4457 |
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| author | Antoni Jaszcz Marta Włodarczyk-Sielicka Andrzej Stateczny Dawid Połap Ilona Garczyńska |
| author_facet | Antoni Jaszcz Marta Włodarczyk-Sielicka Andrzej Stateczny Dawid Połap Ilona Garczyńska |
| author_sort | Antoni Jaszcz |
| collection | DOAJ |
| description | Generating aerial shoreline segmentation masks can be a daunting task, often requiring manual labeling or correction. This is further problematic because neural segmentation models require decent and abundant data for training, requiring even more manpower to automate the process. In this paper, we propose utilizing Unmanned Surface Vehicles (USVs) in an automated shoreline segmentation system on satellite imagery. The remotely controlled vessel first collects above- and underwater shoreline information using light detection and ranging (LiDAR) and multibeam echosounder (MBES) measuring instruments, resulting in a geo-referenced 3D point cloud. After cleaning and processing these data, the system integrates the projected map with an aerial image of the region. Based on the height values of the mapped points, the image is segmented. Finally, post-processing methods and the k-NN algorithm are introduced, resulting in a complete binary shoreline segmentation mask. The obtained data were used for training U-Net-type segmentation models with pre-trained backbones. The InceptionV3-based model achieved an accuracy of 96% and a dice coefficient score of 93%, demonstrating the effectiveness of the proposed system as a source of data acquisition for training deep neural networks. |
| format | Article |
| id | doaj-art-6f60523b522440bf908ca91c7febc8bc |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-6f60523b522440bf908ca91c7febc8bc2025-08-20T01:55:41ZengMDPI AGRemote Sensing2072-42922024-11-011623445710.3390/rs16234457Automated Shoreline Segmentation in Satellite Imagery Using USV MeasurementsAntoni Jaszcz0Marta Włodarczyk-Sielicka1Andrzej Stateczny2Dawid Połap3Ilona Garczyńska4Faculty of Applied Mathematics, Silesian University of Technology, Kaszubska 23, 44-100 Gliwice, PolandDepartment of Geoinformatic and Hydrographic, Maritime University of Szczecin, Wały Chrobrego 1-2, 70-500 Szczecin, PolandFaculty of Navigation, Gdynia Maritime University, 81-87 Morska St., 81-225 Gdynia, PolandFaculty of Applied Mathematics, Silesian University of Technology, Kaszubska 23, 44-100 Gliwice, PolandMarine Technology Ltd., Roszczynialskiego 4/6, 81-521 Gdynia, PolandGenerating aerial shoreline segmentation masks can be a daunting task, often requiring manual labeling or correction. This is further problematic because neural segmentation models require decent and abundant data for training, requiring even more manpower to automate the process. In this paper, we propose utilizing Unmanned Surface Vehicles (USVs) in an automated shoreline segmentation system on satellite imagery. The remotely controlled vessel first collects above- and underwater shoreline information using light detection and ranging (LiDAR) and multibeam echosounder (MBES) measuring instruments, resulting in a geo-referenced 3D point cloud. After cleaning and processing these data, the system integrates the projected map with an aerial image of the region. Based on the height values of the mapped points, the image is segmented. Finally, post-processing methods and the k-NN algorithm are introduced, resulting in a complete binary shoreline segmentation mask. The obtained data were used for training U-Net-type segmentation models with pre-trained backbones. The InceptionV3-based model achieved an accuracy of 96% and a dice coefficient score of 93%, demonstrating the effectiveness of the proposed system as a source of data acquisition for training deep neural networks.https://www.mdpi.com/2072-4292/16/23/4457USVLiDARmultibeam echosounderimagefusionmasks |
| spellingShingle | Antoni Jaszcz Marta Włodarczyk-Sielicka Andrzej Stateczny Dawid Połap Ilona Garczyńska Automated Shoreline Segmentation in Satellite Imagery Using USV Measurements Remote Sensing USV LiDAR multibeam echosounder image fusion masks |
| title | Automated Shoreline Segmentation in Satellite Imagery Using USV Measurements |
| title_full | Automated Shoreline Segmentation in Satellite Imagery Using USV Measurements |
| title_fullStr | Automated Shoreline Segmentation in Satellite Imagery Using USV Measurements |
| title_full_unstemmed | Automated Shoreline Segmentation in Satellite Imagery Using USV Measurements |
| title_short | Automated Shoreline Segmentation in Satellite Imagery Using USV Measurements |
| title_sort | automated shoreline segmentation in satellite imagery using usv measurements |
| topic | USV LiDAR multibeam echosounder image fusion masks |
| url | https://www.mdpi.com/2072-4292/16/23/4457 |
| work_keys_str_mv | AT antonijaszcz automatedshorelinesegmentationinsatelliteimageryusingusvmeasurements AT martawłodarczyksielicka automatedshorelinesegmentationinsatelliteimageryusingusvmeasurements AT andrzejstateczny automatedshorelinesegmentationinsatelliteimageryusingusvmeasurements AT dawidpołap automatedshorelinesegmentationinsatelliteimageryusingusvmeasurements AT ilonagarczynska automatedshorelinesegmentationinsatelliteimageryusingusvmeasurements |