Super-resolution reconstruction of the 1 arc-second Australian coastal DEM dataset

Seafloor mapping to create a coastal DEM dataset of the oceans is significant for various applications. Land-based DEM monitoring and acquisition are more practical as compared to ocean DEM. The highest resolution free available DEM data in the Australian land area is 30 m (1 arc-second), whereas for the ocean area, it is only 450 meters (15 arc-seconds) due to the slow and costly nature of sonar data collection. Deep learning-based image super-resolution (SR) has demonstrated high computational efficiency and optimal performance in enhancing bathymetric resolution. However, direct training a robust network for the entire Australian DEM-SR remains a challenge due to the difficulty of obtaining sufficiently high-resolution ocean DEM data. Therefore, we proposed a novel method that incorporates two key measures to address this issue. First, we designed a deep gradient prior AUSDEM-SRNet network based on numerous land sample sets to acquire prior knowledge from land data. Second, we introduced transfer learning to leverage the similarities between land and bathymetric (ocean) data, compensating for the scarcity of high-resolution bathymetric information. Through experiments using bathymetric (ocean) data around Australia, the proposed method demonstrated superior performance compared to naive bicubic interpolation, achieving a 11.64% reduction in root mean square error (RMSE) and a 1.93% increase in peak signal-to-noise ratio (PSNR) averages. The AUSDEM-SRNet model has been meticulously developed and evaluated for its capability to amplify the spatial resolution of existing DEM dataset by up to 15 times without requiring additional data or information related to the original dataset. As a result, we generated an open-access Australian coastal DEM dataset with 1 arc-second, named AUSDEM_2023. This method can significantly reduce the number of sea areas or points that must be measured, thereby accelerating the detailed mapping of the coastal seabed and the creation of high-resolution sounding maps for various coastal countries.

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Publication Details

Journal
Geo-spatial Information Science
Published
2025-04-08
DOI
https://doi.org/10.1080/10095020.2025.2487143
Primary Topic
Seismic Imaging and Inversion Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Super-resolution reconstruction of the 1 arc-second Australian coastal DEM dataset

Zekai Shi, Danfeng Hong, Bo Zhang, Qi Wang et al.
Geo-spatial Information Science
Seismic Imaging and Inversion Techniques
article

Super-resolution reconstruction of the 1 arc-second Australian coastal DEM dataset

Zekai Shi, Danfeng Hong, Bo Zhang, Qi Wang, Meng Zhang, Haoyuan Ren, Jian Yang
article en

Abstract

Seafloor mapping to create a coastal DEM dataset of the oceans is significant for various applications. Land-based DEM monitoring and acquisition are more practical as compared to ocean DEM. The highest resolution free available DEM data in the Australian land area is 30 m (1 arc-second), whereas for the ocean area, it is only 450 meters (15 arc-seconds) due to the slow and costly nature of sonar data collection. Deep learning-based image super-resolution (SR) has demonstrated high computational efficiency and optimal performance in enhancing bathymetric resolution. However, direct training a robust network for the entire Australian DEM-SR remains a challenge due to the difficulty of obtaining sufficiently high-resolution ocean DEM data. Therefore, we proposed a novel method that incorporates two key measures to address this issue. First, we designed a deep gradient prior AUSDEM-SRNet network based on numerous land sample sets to acquire prior knowledge from land data. Second, we introduced transfer learning to leverage the similarities between land and bathymetric (ocean) data, compensating for the scarcity of high-resolution bathymetric information. Through experiments using bathymetric (ocean) data around Australia, the proposed method demonstrated superior performance compared to naive bicubic interpolation, achieving a 11.64% reduction in root mean square error (RMSE) and a 1.93% increase in peak signal-to-noise ratio (PSNR) averages. The AUSDEM-SRNet model has been meticulously developed and evaluated for its capability to amplify the spatial resolution of existing DEM dataset by up to 15 times without requiring additional data or information related to the original dataset. As a result, we generated an open-access Australian coastal DEM dataset with 1 arc-second, named AUSDEM_2023. This method can significantly reduce the number of sea areas or points that must be measured, thereby accelerating the detailed mapping of the coastal seabed and the creation of high-resolution sounding maps for various coastal countries.

Geo-spatial Information ScienceVol. 28(6)
PLA Information Engineering University (CN), Northwestern Polytechnical University (CN), Chinese Academy of Sciences (CN), Xi'an Jiaotong University (CN)
Natural Science Foundation for Young Scientists of Shanxi Province, Natural Science Basic Research Program of Shaanxi Province
Life below water
Openalex Percentile: Top 90%
Seismic Imaging and Inversion Techniques
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