Bottom Detection from Backscatter Data of Conventional Side Scan Sonars through 1D-UNet
As widely applicated in many underwater research fields, conventional side-scan sonars require the sonar height to be at the seabed for geocoding seabed images. However, many interference factors, including compensation with unknown gains, suspended matters, etc., would bring difficulties in bottom...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/5/1024 |
| _version_ | 1797542211750985728 |
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| author | Jun Yan Junxia Meng Jianhu Zhao |
| author_facet | Jun Yan Junxia Meng Jianhu Zhao |
| author_sort | Jun Yan |
| collection | DOAJ |
| description | As widely applicated in many underwater research fields, conventional side-scan sonars require the sonar height to be at the seabed for geocoding seabed images. However, many interference factors, including compensation with unknown gains, suspended matters, etc., would bring difficulties in bottom detection. Existing methods need manual parameter setups or to use postprocessing methods, which limits automatic and real-time processing in complex situations. To solve this problem, a one-dimensional U-Net (1D-UNet) model for sea bottom detection of side-scan data and the bottom detection and tracking method based on 1D-UNet are proposed in this work. First, the basic theory of sonar bottom detection and the interference factors is introduced, which indicates that deep learning of the bottom is a feasible solution. Then, a 1D-UNet model for detecting the sea bottom position from the side-scan backscatter strength sequences is proposed, and the structure and implementation of this model are illustrated in detail. Finally, the bottom detection and tracking algorithms of a single ping and continuous pings are presented on the basis of the proposed model. The measured side-scan sonar data in Meizhou Bay and Bayuquan District were selected in the experiments to verify the model and methods. The 1D-UNet model was first trained and applied with the side-scan data in Meizhou Bay. The training and validation accuracies were 99.92% and 99.77%, respectively, and the sea bottom detection accuracy of the training survey line was 99.88%. The 1D-UNet model showed good robustness to the interference factors of bottom detection and fully real-time performance in comparison with other methods. Moreover, the trained 1D-UNet model is used to process the data in the Bayuquan District for proving model generality. The proposed 1D-UNet model for bottom detection has been proven effective for side-scan sonar data and also has great potentials in wider applications on other types of sonars. |
| first_indexed | 2024-03-10T13:26:30Z |
| format | Article |
| id | doaj.art-cfff855b4340446d81fd22b04f608207 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T13:26:30Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-cfff855b4340446d81fd22b04f6082072023-11-21T09:37:11ZengMDPI AGRemote Sensing2072-42922021-03-01135102410.3390/rs13051024Bottom Detection from Backscatter Data of Conventional Side Scan Sonars through 1D-UNetJun Yan0Junxia Meng1Jianhu Zhao2School of Resources and Environmental Engineering, Anhui University, Hefei 230601, ChinaCollege of Civil Engineering, Anhui Jianzhu University, Hefei 230601, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan 430079, ChinaAs widely applicated in many underwater research fields, conventional side-scan sonars require the sonar height to be at the seabed for geocoding seabed images. However, many interference factors, including compensation with unknown gains, suspended matters, etc., would bring difficulties in bottom detection. Existing methods need manual parameter setups or to use postprocessing methods, which limits automatic and real-time processing in complex situations. To solve this problem, a one-dimensional U-Net (1D-UNet) model for sea bottom detection of side-scan data and the bottom detection and tracking method based on 1D-UNet are proposed in this work. First, the basic theory of sonar bottom detection and the interference factors is introduced, which indicates that deep learning of the bottom is a feasible solution. Then, a 1D-UNet model for detecting the sea bottom position from the side-scan backscatter strength sequences is proposed, and the structure and implementation of this model are illustrated in detail. Finally, the bottom detection and tracking algorithms of a single ping and continuous pings are presented on the basis of the proposed model. The measured side-scan sonar data in Meizhou Bay and Bayuquan District were selected in the experiments to verify the model and methods. The 1D-UNet model was first trained and applied with the side-scan data in Meizhou Bay. The training and validation accuracies were 99.92% and 99.77%, respectively, and the sea bottom detection accuracy of the training survey line was 99.88%. The 1D-UNet model showed good robustness to the interference factors of bottom detection and fully real-time performance in comparison with other methods. Moreover, the trained 1D-UNet model is used to process the data in the Bayuquan District for proving model generality. The proposed 1D-UNet model for bottom detection has been proven effective for side-scan sonar data and also has great potentials in wider applications on other types of sonars.https://www.mdpi.com/2072-4292/13/5/1024side-scan sonarsea bottom detection1D-UNetsignal segmentationreal-time processing |
| spellingShingle | Jun Yan Junxia Meng Jianhu Zhao Bottom Detection from Backscatter Data of Conventional Side Scan Sonars through 1D-UNet Remote Sensing side-scan sonar sea bottom detection 1D-UNet signal segmentation real-time processing |
| title | Bottom Detection from Backscatter Data of Conventional Side Scan Sonars through 1D-UNet |
| title_full | Bottom Detection from Backscatter Data of Conventional Side Scan Sonars through 1D-UNet |
| title_fullStr | Bottom Detection from Backscatter Data of Conventional Side Scan Sonars through 1D-UNet |
| title_full_unstemmed | Bottom Detection from Backscatter Data of Conventional Side Scan Sonars through 1D-UNet |
| title_short | Bottom Detection from Backscatter Data of Conventional Side Scan Sonars through 1D-UNet |
| title_sort | bottom detection from backscatter data of conventional side scan sonars through 1d unet |
| topic | side-scan sonar sea bottom detection 1D-UNet signal segmentation real-time processing |
| url | https://www.mdpi.com/2072-4292/13/5/1024 |
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