Shallow water bathymetry mapping using Support Vector Machine (SVM) technique and multispectral imagery

ABSTRACT

Satellite imagery along with image processing techniques prove to be efficient tools for bathymetry retrieval as they provide time and cost-effective alternatives to traditional methods of water depth estimation. In this article, a nonlinear machine learning technique of Support Vector Machine (SVM) is used to derive shallow water bathymetry data along Sint Maarten Island and Ameland Inlet, The Netherlands, by combining echo-sounding measurements and the reflectance of blue, green, or red bands of Landsat Enhanced Thematic Mapper Plus (Landsat 7 ETM+) and Landsat 8 Operational Land Imager (OLI) imagery with 30 m spatial resolution. In the analysis, 80% of data points of the echo-sounding measurements are used for training and the remaining 20% data points are used for testing. The model utilizes the radial basis kernel function (nonlinear) and the other training factors such as the smoothing parameter, penalty parameter C, and insensitivity zone ε are selected and tuned based on the learning (i.e. training) process. The overall errors during test phases for Sint Maarten Island (1–15 m) and Ameland Inlet (1.00–3.50 m) are 8.26% and 14.43%, respectively, reflecting that the model produces significant estimations for the shallow depths ranges, considered in this study. The results obtained are also compared statistically with those estimated from the widely used linear transform model and ratio transform model, which establish a linear relationship between the water depth and band reflectances. Based on the results, it is evident that SVM provides a comparable or better performance for shallow depth ranges and can be used effectively for deriving accurate and updated medium resolution bathymetric maps.

Acknowledgments

The authors would like to thank Council of Scientific & Industrial Research – CSIR for providing the funding for Ms Ankita Misra, who is CSIR-Senior Research Fellow (SRF) at Indian Institute of Technology-Bombay, Mumbai, India. This work has been carried out as a part of her doctoral research. RR is supported by the AXA Research fund and the Deltares Harbour, Coastal and Offshore engineering Research Programme ‘Bouwen aan de Kust.’

Conflict of interest

The authors clearly declare that they have no conflict of interest and Informed consent was obtained from all the individual participants and co-authors.

Disclosure statement

No potential conflict of interest was reported by the authors.