A Mapping Method Fusing Forward-Looking Sonar and Side-Scan Sonar
In modern ocean exploration, forward-looking sonar (FLS) provides real-time 2D imaging of the seabed ahead, but its detection range is relatively limited. Conversely, side-scan sonar (SSS) enables large-scale imaging of the seabed during movement but struggles to effectively image areas directly ben...
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MDPI AG
2025-01-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/1/166 |
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| author | Hong Liu Xiufen Ye Hanwen Zhou Hanjie Huang |
| author_facet | Hong Liu Xiufen Ye Hanwen Zhou Hanjie Huang |
| author_sort | Hong Liu |
| collection | DOAJ |
| description | In modern ocean exploration, forward-looking sonar (FLS) provides real-time 2D imaging of the seabed ahead, but its detection range is relatively limited. Conversely, side-scan sonar (SSS) enables large-scale imaging of the seabed during movement but struggles to effectively image areas directly beneath the sensor. Integrating FLS and SSS offers a promising solution by leveraging their complementary strengths to achieve comprehensive seabed mapping. However, no prior research has explored this fusion approach. This paper presents a novel method for FLS and SSS fusion mapping. Firstly, a novel sonar image enhancement method based on equalization is proposed, enabling simultaneous enhancement and grayscale unification of two sonar images. Additionally, an effective area extraction approach for FLS images, grounded on the approximate erosion method, is introduced to produce high-quality FLS mapping. Furthermore, by examining the data distribution in FLS and SSS mappings, the standard deviation of these datasets is utilized to refine the grayscale distribution of FLS mapping, thereby enhancing the grayscale distribution similarity between the two mapping results. Finally, FLS map data are seamlessly integrated into the gaps of the SSS map, resulting in a fused, comprehensive seabed representation. Large-scale experiments demonstrate that the proposed method effectively combines the strengths of FLS and SSS, producing complete and detailed seabed topography maps. Simultaneously, numerous ablation experiments are conducted to evaluate the impact of various parameters on fusion mapping, providing guidelines for selecting the optimal parameters. This fusion approach, thus, holds significant practical value for ocean exploration and seabed mapping applications. |
| format | Article |
| id | doaj-art-891aadfe924b4c4c878e3d0010c9b589 |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-891aadfe924b4c4c878e3d0010c9b5892025-01-24T13:37:06ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-01-0113116610.3390/jmse13010166A Mapping Method Fusing Forward-Looking Sonar and Side-Scan SonarHong Liu0Xiufen Ye1Hanwen Zhou2Hanjie Huang3College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, ChinaIn modern ocean exploration, forward-looking sonar (FLS) provides real-time 2D imaging of the seabed ahead, but its detection range is relatively limited. Conversely, side-scan sonar (SSS) enables large-scale imaging of the seabed during movement but struggles to effectively image areas directly beneath the sensor. Integrating FLS and SSS offers a promising solution by leveraging their complementary strengths to achieve comprehensive seabed mapping. However, no prior research has explored this fusion approach. This paper presents a novel method for FLS and SSS fusion mapping. Firstly, a novel sonar image enhancement method based on equalization is proposed, enabling simultaneous enhancement and grayscale unification of two sonar images. Additionally, an effective area extraction approach for FLS images, grounded on the approximate erosion method, is introduced to produce high-quality FLS mapping. Furthermore, by examining the data distribution in FLS and SSS mappings, the standard deviation of these datasets is utilized to refine the grayscale distribution of FLS mapping, thereby enhancing the grayscale distribution similarity between the two mapping results. Finally, FLS map data are seamlessly integrated into the gaps of the SSS map, resulting in a fused, comprehensive seabed representation. Large-scale experiments demonstrate that the proposed method effectively combines the strengths of FLS and SSS, producing complete and detailed seabed topography maps. Simultaneously, numerous ablation experiments are conducted to evaluate the impact of various parameters on fusion mapping, providing guidelines for selecting the optimal parameters. This fusion approach, thus, holds significant practical value for ocean exploration and seabed mapping applications.https://www.mdpi.com/2077-1312/13/1/166forward-looking sonarside-scan sonarsonar fusion mappingimage equalization |
| spellingShingle | Hong Liu Xiufen Ye Hanwen Zhou Hanjie Huang A Mapping Method Fusing Forward-Looking Sonar and Side-Scan Sonar Journal of Marine Science and Engineering forward-looking sonar side-scan sonar sonar fusion mapping image equalization |
| title | A Mapping Method Fusing Forward-Looking Sonar and Side-Scan Sonar |
| title_full | A Mapping Method Fusing Forward-Looking Sonar and Side-Scan Sonar |
| title_fullStr | A Mapping Method Fusing Forward-Looking Sonar and Side-Scan Sonar |
| title_full_unstemmed | A Mapping Method Fusing Forward-Looking Sonar and Side-Scan Sonar |
| title_short | A Mapping Method Fusing Forward-Looking Sonar and Side-Scan Sonar |
| title_sort | mapping method fusing forward looking sonar and side scan sonar |
| topic | forward-looking sonar side-scan sonar sonar fusion mapping image equalization |
| url | https://www.mdpi.com/2077-1312/13/1/166 |
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