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A VGGNet-Based Method for Refined Bathymetry from Satellite Altimetry to Reduce Errors.

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Title: A VGGNet-Based Method for Refined Bathymetry from Satellite Altimetry to Reduce Errors.
Authors: Chen, Xiaolun1 (AUTHOR), Luo, Xiaowen1,2,3 (AUTHOR) luoxiaowen@sio.org.cn, Wu, Ziyin1,2,4 (AUTHOR), Qin, Xiaoming1,2 (AUTHOR), Shang, Jihong1 (AUTHOR), Li, Bin5 (AUTHOR), Wang, Mingwei1 (AUTHOR), Wan, Hongyang1 (AUTHOR)
Source: Remote Sensing. Dec2022, Vol. 14 Issue 23, p5939. 16p.
Subjects: Bathymetry, Multibeam mapping, Altimetry, Standard deviations, Water depth, Echo sounding, Submarine topography
Abstract: Only approximately 20% of the global seafloor topography has been finely modeled. The rest either lacks data or its data are not accurate enough to meet practical requirements. On the one hand, the satellite altimeter has the advantages of large-scale and real-time observation. Therefore, it is widely used to measure bathymetry, the core of seafloor topography. However, there is often room to improve its precision. Multibeam sonar bathymetry is more precise but generally limited to a smaller coverage, so it is in a complementary relationship with the satellite-derived bathymetry. To combine the advantages of satellite altimetry-derived and multibeam sonar-derived bathymetry, we apply deep learning to perform multibeam sonar-based bathymetry correction for satellite altimetry bathymetry data. Specifically, we modify a pretrained VGGNet neural network model to train on three sets of bathymetry data from the West Pacific, Southern Ocean, and East Pacific. Experiments show that the correlation of bathymetry data before and after correction can reach a high level, with the performance of R2 being as high as 0.81, and the normalized root-mean-square deviation (NRMSE) improved by over 19% compared with previous research. We then explore the relationship between R2 and water depth and conclude that it varies at different depths. Thus, the terrain specificity is a factor that affects the precision of the correction. Finally, we apply the difference in water depth before and after the correction for evaluation and find that our method can improve by more than 17% compared with previous research. The results show that the VGGNet model can perform better correction to the bathymetry data. Hence, we provide a novel method for accurate modeling of the seafloor topography. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A VGGNet-Based Method for Refined Bathymetry from Satellite Altimetry to Reduce Errors.
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  Data: <searchLink fieldCode="AR" term="%22Chen%2C+Xiaolun%22">Chen, Xiaolun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Luo%2C+Xiaowen%22">Luo, Xiaowen</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> luoxiaowen@sio.org.cn</i><br /><searchLink fieldCode="AR" term="%22Wu%2C+Ziyin%22">Wu, Ziyin</searchLink><relatesTo>1,2,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qin%2C+Xiaoming%22">Qin, Xiaoming</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shang%2C+Jihong%22">Shang, Jihong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Bin%22">Li, Bin</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Mingwei%22">Wang, Mingwei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wan%2C+Hongyang%22">Wan, Hongyang</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Dec2022, Vol. 14 Issue 23, p5939. 16p.
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  Data: <searchLink fieldCode="DE" term="%22Bathymetry%22">Bathymetry</searchLink><br /><searchLink fieldCode="DE" term="%22Multibeam+mapping%22">Multibeam mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Altimetry%22">Altimetry</searchLink><br /><searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink><br /><searchLink fieldCode="DE" term="%22Water+depth%22">Water depth</searchLink><br /><searchLink fieldCode="DE" term="%22Echo+sounding%22">Echo sounding</searchLink><br /><searchLink fieldCode="DE" term="%22Submarine+topography%22">Submarine topography</searchLink>
– Name: Abstract
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  Data: Only approximately 20% of the global seafloor topography has been finely modeled. The rest either lacks data or its data are not accurate enough to meet practical requirements. On the one hand, the satellite altimeter has the advantages of large-scale and real-time observation. Therefore, it is widely used to measure bathymetry, the core of seafloor topography. However, there is often room to improve its precision. Multibeam sonar bathymetry is more precise but generally limited to a smaller coverage, so it is in a complementary relationship with the satellite-derived bathymetry. To combine the advantages of satellite altimetry-derived and multibeam sonar-derived bathymetry, we apply deep learning to perform multibeam sonar-based bathymetry correction for satellite altimetry bathymetry data. Specifically, we modify a pretrained VGGNet neural network model to train on three sets of bathymetry data from the West Pacific, Southern Ocean, and East Pacific. Experiments show that the correlation of bathymetry data before and after correction can reach a high level, with the performance of R2 being as high as 0.81, and the normalized root-mean-square deviation (NRMSE) improved by over 19% compared with previous research. We then explore the relationship between R2 and water depth and conclude that it varies at different depths. Thus, the terrain specificity is a factor that affects the precision of the correction. Finally, we apply the difference in water depth before and after the correction for evaluation and find that our method can improve by more than 17% compared with previous research. The results show that the VGGNet model can perform better correction to the bathymetry data. Hence, we provide a novel method for accurate modeling of the seafloor topography. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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        PageCount: 16
        StartPage: 5939
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      – SubjectFull: Bathymetry
        Type: general
      – SubjectFull: Multibeam mapping
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      – SubjectFull: Altimetry
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      – SubjectFull: Standard deviations
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      – SubjectFull: Water depth
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      – SubjectFull: Echo sounding
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      – SubjectFull: Submarine topography
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      – TitleFull: A VGGNet-Based Method for Refined Bathymetry from Satellite Altimetry to Reduce Errors.
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              M: 12
              Text: Dec2022
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