MBES-DDPM: Multibeam Echo Sounder Bathymetry Swath Gap Reconstruction Based on Denoising Diffusion Probability Model.
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| Title: | MBES-DDPM: Multibeam Echo Sounder Bathymetry Swath Gap Reconstruction Based on Denoising Diffusion Probability Model. |
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| Authors: | Chen, Jianbing1,2 (AUTHOR), Wu, Ziyin1,2,3 (AUTHOR) zywu@sio.org.cn, Zhao, Dineng2,3 (AUTHOR), Bu, Xianhai1,4 (AUTHOR), Zhou, Jieqiong1,2 (AUTHOR), Shang, Jihong2 (AUTHOR), Wang, Mingwei2,3 (AUTHOR), Liu, Yang2,4 (AUTHOR) |
| Source: | Remote Sensing. Feb2026, Vol. 18 Issue 3, p496. 29p. |
| Subjects: | Bathymetry, Generative artificial intelligence, Multisensor data fusion, Oceanographic maps, Sonar |
| Abstract: | Highlights: What are the main findings? A novel generative AI model, MBES-DDPM, is proposed, which is the first application of a denoising diffusion probabilistic model (DDPM) for restoring swath gaps in multibeam echo sounder (MBES) bathymetric data. MBES-DDPM achieves superior performance, demonstrating an average reduction in RMSE of at least 34.21% and an average increase in PSNR of over 3.71 dB compared to baseline methods, while best preserving terrain slope accuracy. What are the implications of the main findings? This research establishes a pioneering framework that demonstrates the significant potential of advanced generative AI paradigms, coupled with multisource geophysical data fusion, for solving key reconstruction challenges in underwater remote sensing. It will contribute to the advancement of the Seabed 2030 Project and enhance the quality of seamless global seafloor topography modeling. The multibeam echo sounder (MBES) is a key tool for acquiring high-precision seabed topographic data. However, measurement gaps resulting from its swath-based measurement mode are prevalent, severely compromising the completeness of seabed terrain modeling. To address this issue, this study first categorizes multibeam data gaps into two data degradation patterns with clear hydrographic survey backgrounds: "random degradation" and "rule-based degradation." Based on this categorization, a highly realistic training dataset that closely matches actual conditions is constructed. To improve the reconstruction accuracy and topographic fidelity, a novel multibeam echo sounder data reconstruction model, the MBES-DDPM, is proposed. Based on the denoising diffusion probabilistic model (DDPM) framework, this model innovatively incorporates gravity anomaly data as prior knowledge. Then, with a designed multisource data fusion guidance mechanism, macro-topographic structural constraints are injected during the diffusion process. Furthermore, a targeted quantitative and qualitative evaluation system is established. The experimental results show that compared with the baseline methods, the MBES-DDPM achieves the best performance across various complex scenarios. Its restored results exhibit an average reduction in root mean square error of at least 34.21% and an average increase in peak signal-to-noise ratio of more than 3.71 dB. Furthermore, it achieves the highest reconstruction fidelity in teams of the terrain slope accuracy metrics. Thus, this research provides a new and reliable solution for accurately restoring large-scale MBES data. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A novel generative AI model, MBES-DDPM, is proposed, which is the first application of a denoising diffusion probabilistic model (DDPM) for restoring swath gaps in multibeam echo sounder (MBES) bathymetric data. MBES-DDPM achieves superior performance, demonstrating an average reduction in RMSE of at least 34.21% and an average increase in PSNR of over 3.71 dB compared to baseline methods, while best preserving terrain slope accuracy. What are the implications of the main findings? This research establishes a pioneering framework that demonstrates the significant potential of advanced generative AI paradigms, coupled with multisource geophysical data fusion, for solving key reconstruction challenges in underwater remote sensing. It will contribute to the advancement of the Seabed 2030 Project and enhance the quality of seamless global seafloor topography modeling. The multibeam echo sounder (MBES) is a key tool for acquiring high-precision seabed topographic data. However, measurement gaps resulting from its swath-based measurement mode are prevalent, severely compromising the completeness of seabed terrain modeling. To address this issue, this study first categorizes multibeam data gaps into two data degradation patterns with clear hydrographic survey backgrounds: "random degradation" and "rule-based degradation." Based on this categorization, a highly realistic training dataset that closely matches actual conditions is constructed. To improve the reconstruction accuracy and topographic fidelity, a novel multibeam echo sounder data reconstruction model, the MBES-DDPM, is proposed. Based on the denoising diffusion probabilistic model (DDPM) framework, this model innovatively incorporates gravity anomaly data as prior knowledge. Then, with a designed multisource data fusion guidance mechanism, macro-topographic structural constraints are injected during the diffusion process. Furthermore, a targeted quantitative and qualitative evaluation system is established. The experimental results show that compared with the baseline methods, the MBES-DDPM achieves the best performance across various complex scenarios. Its restored results exhibit an average reduction in root mean square error of at least 34.21% and an average increase in peak signal-to-noise ratio of more than 3.71 dB. Furthermore, it achieves the highest reconstruction fidelity in teams of the terrain slope accuracy metrics. Thus, this research provides a new and reliable solution for accurately restoring large-scale MBES data. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18030496 |
