Automatic Waterline Extraction and Topographic Mapping of Tidal Flats From SAR Images Based on Deep Learning

Abstract This study presented an intuitive approach to derive large‐scale tidal flat's Digital Elevation Model (DEM). We first developed an automated method for accurately extracting the waterline from Synthetic Aperture Radar images acquired in Subei Sandbanks along the Yellow Sea coast of China between 2015 and 2020 based on deep convolutional neural networks. The statistical results show this method has appreciable accuracy for efficient waterline extraction even under complex imaging conditions with a mean recall and precision of 0.90 and 0.80, respectively. Then the pixel‐level extracted waterlines are calibrated with a global tide model to construct the large‐scale tidal flat's DEM in the study region. The comparison against in situ topographic data shows an error of 29 cm, demonstrating the usefulness of monitoring the morpho‐sedimentary evolution in intertidal areas. Furthermore, the Subei Sandbanks remained stable from 2015 to 2020, while the coastal region changed drastically due to human activities.