An automatic shoreline extraction method from SAR imagery using DeepLab-v3+ and its versatility

Abstract

ABSTRACT This study presented an automatic shoreline extraction method from synthetic aperture radar (SAR) imagery using a semantic image segmentation model, DeepLab-v3+. Shorelines extracted from optical satellite images were employed as ground truth. More than 130,000 pairs of labeled images were generated, which substantially enhances the dataset’s objectivity for model training. By using different combination of SAR images from beaches with varying characteristics, several models were constructed. These models were then applied to 15 beaches of Japan to validate their accuracy and versatility. The developed machine learning model demonstrates high accuracy in shoreline extraction, particularly when trained with a diverse set of images from various beaches. This versatility is crucial for the model’s applicability across different coastal environments, underscoring the importance of incorporating a wide range of beach properties into the training dataset.