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

Jiaxin Wan,Yi Ma

doi:10.1007/s12524-020-01255-9

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.

Highlights

Water depth is one of the important parameters of the marine environment
It is clear that deep belief network with data perturbation (DBN-DP) is most effective with 0.80 R2 and 0.9 m median absolute error (MedAE)
In order to further verify the availability of the Deep belief network (DBN)-DP model, another study area is selected in Yongxing Island, China

Summary

Introduction

Water depth is one of the important parameters of the marine environment. It is of great significance for maritime transportation, coastal management, and coral reef ecosystem protection. Shipborne sonar measurement and airborne light detection and ranging (LiDAR) measurement have produced quality water depth data. Multi-beam Sonar measurement can acquire accurate data that meet the chart measurement standard, but it is time-consuming and demanding. While the airborne LiDAR can collects accurate bathymetry data fast and safely, especially in areas where sonar is not available (Guenther 2007), it still suffers from a number of drawbacks, such as limited areal coverage, complexity in operation and costly. Remote sensing can provide large-scale and high spatial/temporalresolution data, which makes it an emerging technique for bathymetry inversion. SDB has been used to help NOAA update nautical charts at higher frequencies (Pe’Eri et al 2014).Under optimal clear water conditions, SDB is commonly employed for depths of 0–30 m

Methods

Results

Discussion

Conclusion