HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline

• Main Authors: Heidler, Konrad, Mou, Lichao, Baumhoer, Celia, Dietz, Andreas, Zhu, Xiao Xiang
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: arXiv 2021
• Subjects: Computer Vision and Pattern Recognition cs.CV; Image and Video Processing eess.IV; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Antarctic; The Antarctic; Antarc*
• Online Access: https://dx.doi.org/10.48550/arxiv.2103.01849; https://arxiv.org/abs/2103.01849

Deep learning-based coastline detection algorithms have begun to outshine traditional statistical methods in recent years. However, they are usually trained only as single-purpose models to either segment land and water or delineate the coastline. In contrast to this, a human annotator will usually keep a mental map of both segmentation and delineation when performing manual coastline detection. To take into account this task duality, we therefore devise a new model to unite these two approaches in a deep learning model. By taking inspiration from the main building blocks of a semantic segmentation framework (UNet) and an edge detection framework (HED), both tasks are combined in a natural way. Training is made efficient by employing deep supervision on side predictions at multiple resolutions. Finally, a hierarchical attention mechanism is introduced to adaptively merge these multiscale predictions into the final model output. The advantages of this approach over other traditional and deep learning-based methods for coastline detection are demonstrated on a dataset of Sentinel-1 imagery covering parts of the Antarctic coast, where coastline detection is notoriously difficult. An implementation of our method is available at \url{https://github.com/khdlr/HED-UNet}. : This work has been accepted by IEEE TGRS for publication. Copyright may be transferred without notice, after which this version may no longer be accessible