A deep learning method for nearshore bathymetry with ICESat-2 and Sentinel-2 datasets

doi:10.13474/j.cnki.11-2246.2023.0198

Abstract

Abstract: Currently, satellite-derived bathymetry (SDB) is widely used for nearshore bathymetry. However, the commonly used empirical models are too simple to be applied to various complex shore environments. To break through the limitations of traditional methods, this paper proposes a deep learning method for nearshore bathymetry with ICESat-2 and Sentinel-2 datasets. Cat Islands (CI) and Buck Island (BI) are used as the study areas. ICESat-2 is used to extract a priori bathymetry points, and then a one dimensional convolutional neural network(1DCNN) is trained on the Sentinel-2 data to obtain a bathymetry map of the study area. band ratio (BR), random forest (RF) and multilayer perceptron (MLP) are also used as comparison methods. Through quantitative analysis of accuracy, the root mean square error and coefficient of determination of water depth measured by the proposed method in CI and BI are 0.20 m, 0.94 and 0.95 m, 0.95, respectively, which verify the accuracy better than other comparative methods and improved the accuracy of water depth inversion.

Key words: satellite-derived bathymetry, ICESat-2, Sentinel-2, deep learning, machine learning

CLC Number:

P237

ZHONG Jing, SUN Jie, LAI Zulong, SHEN Yifu. A deep learning method for nearshore bathymetry with ICESat-2 and Sentinel-2 datasets[J]. Bulletin of Surveying and Mapping, 2023, 0(7): 39-43.

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