Shallow water bathymetry retrieving of optical remote sensing combined with SVM bottom classification

Remote sensing bathymetry has become one of the main methods for shallow water bathymetry with large range, flexibility and economy. The bottom of shallow water is often complex and diverse, and different types of sea bottom have different effects on the bathymetry retrieving. Bottom classification is one of the methods to improve the accuracy of water depth retrieving. In this paper, GeoEye-1 multispectral remote sensing image and in-situ water depth data are used for bathymetry retrieving with bottom classification in the shallow water around Robert Island. Main works include data preparation, SVM bottom classification, water depth model construction and verification. The experimental results show that the accuracy of sub-area model is higher than that of whole-area model without bottom classification. Bottom classification can improve the retrieving accuracy of Stumpf ratio model and Lyzenga logarithmic model in Robert Island, but its improvement effect on the former is less than that of the latter. Stumpf ratio model can weaken the adverse influence of complex bottom on bathymetry retrieving, so it is more suitable for large area and complex bottom waters.