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Articles

Comparison of Six Empirical Methods for Multispectral Satellite-derived Bathymetry

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Pages 149-174 | Received 07 Jun 2022, Accepted 29 Sep 2022, Published online: 21 Oct 2022
 

Abstract

Satellite-derived bathymetry (SDB), an important technology in marine geodesy, is advantageous because of its wide coverage, low cost, and short revisit cycle. At present, several different kinds of SDB methods exist, and their inversion accuracy is affected by algorithm performance, band selection, and sample distribution, among other factors. But these factors have not been adequately quantified and compared. In the present study, we evaluate the performances and highlight the best scenarios for applying the six classical empirical methods including the log-transformed single band, band ratio (BR), Lyzenga polynomial (LP), support vector regression, third-order polynomial (TOP), and back propagation (BP) neural network. The results reveal that the number of training samples is important for the empirical SDB methods, and the TOP and BP methods need more training samples than other methods. Compared to the robust BR and LP methods, the TOP and BP methods can obtain high accuracy but are severely influenced by incomplete samples. In addition, experiments that prove the local minimum (poor robustness) problem of the BP method exist and cannot be ignored in the bathymetry field. The present study highlights the most suitable method for obtaining reliable SDB results and their applicability.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (42001401), China Postdoctoral Science Foundation (2020M671431), Fundamental Research Funds for the Central Universities (0209-14380096), and Guangxi Innovative Development Grand Grant (2018AA13005).

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