An Improved Method Based on VGGNet for Refined Bathymetry from Satellite Altimetry: Reducing the Errors Effectively
At present, only approximately 10 % of the global seafloor topography has been finely modeled, and the rest are either lacking in data or not accurate enough to meet practical requirements. On the one hand, satellite altimeter has the advantages of large-scale and real-time observation, thus is wide...
| Main Authors: | , , , , , , |
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| Format: | Text |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/gmd-2022-140 https://gmd.copernicus.org/preprints/gmd-2022-140/ |
| Summary: | At present, only approximately 10 % of the global seafloor topography has been finely modeled, and the rest are either lacking in data or not accurate enough to meet practical requirements. On the one hand, satellite altimeter has the advantages of large-scale and real-time observation, thus is widely used in the measurement of bathymetry, the core of seafloor topography. However, there is often room for improvement in its precision. On the other hand, multibeam echosounder bathymetric data is highly precise but normally limited to a smaller coverage, which forms a complementary relationship with the bathymetry derived from satellite altimetry. To combine the advantages of satellite altimetry-derived and multibeam sonar-derived bathymetry, we apply deep learning, which is powerful in the field of digital image automation, to perform multibeam sonar-based bathymetry correction for satellite altimetry bathymetry data. Specifically, we modify and improve a pretrained VGGNet neural network model with a depth of 19 layers to train on three sets of bathymetry data from the West Pacific, Southern Ocean, and East Pacific, respectively. Experiments show that the correlation of bathymetry data before and after correction can reach a high level, with the performance of R 2 being as high as 0.81 and the RMSE improved over 19 % compared with previous research. We then explore the relationship between R 2 and water depth and conclude that it varies at different depths and thus the terrain specificity was a factor that affects the precision of correction. Finally, we use the difference of water depth before and after the correction to evaluate the correction results, and find that our method can improve by more than 17 % compared with previous research. The results show that using the deep learning VGGNet model can better perform the correction of the bathymetry derived from satellite altimetry, thus providing a method for accurate modeling of the seafloor topography. |
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