Automatic coastline extraction through enhanced sea-land segmentation by modifying Standard U-Net

Abstract

Sea-land segmentation (SLS) is an essential remote sensing task for various coastal and environmental studies such as coastline extraction, coastal erosion, coastal area monitoring, and ship or iceberg detection. This study aims at improving the SLS performance by modifying the Standard U-Net (SUN) model and developing an automatic coastline extraction framework. SUN generally has an acceptable performance in many applications. However, better SLS outputs are needed for reliable coastline extraction. In our proposed framework, we firstly analyzed three different input images, including Red-Green-Blue (RGB), Normalized Difference Water Index (NDWI), and Near-Infrared (NIR) images. Secondly, we modified the SUN architecture to improve the segmentation results. The main modifications are using different loss functions and two fusion methods for RGB and NIR images. The segmentation results were then passed into the subsequent automatic coastline extraction pipeline based on morphological operations and pixel connectivity analysis. The training and testing steps were accomplished utilizing a benchmark dataset of China’s coastal areas. Moreover, another dataset consisting of a time series of Landsat-8 imagery from the southern Caspian Sea coastlines was collected to evaluate coastline extraction efficiency. The results indicate that the proposed modifications could effectively enhance the performance of the SUN, with the most significant improvement to the Intersection over Union (IoU) score being as high as 1.68% and 8.95% in China and Caspian Sea datasets, respectively, while outperforming other state-of-the-art models including FC-DenseNet and DeepLabV3+.