ORCID

0009-0006-8924-4192 (Zhang), 0000-0001-8337-7441 (Hill), 0000-0002-9399-4264 (Zimmerman)

Document Type

Article

Publication Date

2025

DOI

10.3390/geomatics5030034

Publication Title

Geomatics

Volume

5

Issue

3

Pages

34 (1-23)

Abstract

Traditional bathymetry measures require a large number of human hours, and many bathymetry records are obsolete or missing. Automated measures of bathymetry would reduce costs and increase accessibility for research and applications. In this paper, we optimized a recent machine learning model, named CatBoostOpt, to estimate bathymetry based on high-resolution WorldView-2 (WV-2) multi-spectral optical satellite images. CatBoostOpt was demonstrated across the Florida Big Bend coastline, where the model learned correlations between in situ sound Navigation and Ranging (Sonar) bathymetry measurements and the corresponding multi-spectral reflectance values in WV-2 images to map bathymetry. We evaluated three different feature transformations as inputs for bathymetry estimation, including raw reflectance, log-linear, and log-ratio transforms of the raw reflectance value in WV-2 images. In addition, we investigated the contribution of each spectral band and found that utilizing all eight spectral bands in WV-2 images offers the best solution for handling complex water quality conditions. We compared CatBoostOpt with linear regression (LR), support vector machine (SVM), random forest (RF), AdaBoost, gradient boosting, and deep convolutional neural network (DCNN). CatBoostOpt with log-ratio transformed reflectance achieved the best performance with an average root mean square error (RMSE) of 0.34 and coefficient of determination (R2) of 0.87.

Rights

© 2025 by the authors.

The article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

Article states: "In situ bathymetry and model data are available upon request from the authors. Satellite imagery can be obtained from the NASA Commercial Data Buy program or directly from Maxar. Remove the following statement from the Acknowledgements: The data will be made available by the authors on request."

Original Publication Citation

Islam, K. A., Abul-Hassan, O., Zhang, H., Hill, V., Schaeffer, B., Zimmerman, R., & Li, J. (2025). Ensemble machine learning approaches for bathymetry estimation in multi-spectral images. Geomatics, 5(3), 1-23, Article 34. https://doi.org/10.3390/geomatics5030034

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