Shallow Water Bathymetry Mapping of Xinji Island Based on Multispectral Satellite Image using Deep Learning

Abstract

Nearshore bathymetry is a basic parameter of the ocean, which is crucial to the research and management of coastal zones. Previous studies have demonstrated that remote sensing techniques can be employed in estimating bathymetric information. In this paper, we propose a deep belief network with data perturbation (DBN-DP) algorithm for shallow water depth inversion from high resolution multispectral data, and applying it in Xinji Island of Malacca Strait and Yongxing Island in China. Results show that the DBN-DP method can produce more accurate water depth estimations than other traditional methods particularly for deeper water, which reaches 1.2 m of mean absolute error (MAE) and 12.8% of mean relative error (MRE) in Xinji Island. Most of the estimated bathymetry meet the category of zone of confidence C level defined by the International Hydrographic Organization. These findings are encouraging for employing deep learning in bathymetry, which may become a novel approach for bathymetric inversion in the future.