Satellite-Derived Bathymetry Using Deep Convolutional Neural Network

Abstract

Our goal is to develop technique for assessing bathymetry maps, which show the topography of the floors of water-bodies, using satellite or aerial imagery. The advent of deep neural networks has enabled the use of new techniques in analysing and creating depth maps from high resolution satellite images. In this paper we report a pilot study in exploring the potential use of a deep learning architecture by framing bathymetry problem as a pixel-wise classification task. We took the Sentinel-2 bands as the satellite image and independent depth measurements from Humminbird™data. The data for the supervised training was carefully prepared and processed to facilitate the use of a powerful deep learning segmentation models. The efficiency of the model was quantified by the average F1-score (Dice score) on the held out dataset on quantized depth bands.