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

• Published in: IEEE Transactions on Geoscience and Remote Sensing
• Main Authors: Heidler, Konrad, Mou, LiChao, Baumhoer, Celia, Dietz, Andreas, Zhu, Xiao Xiang
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: IEEE - Institute of Electrical and Electronics Engineers 2022
• Subjects: Dynamik der Landoberfläche; EO Data Science; Antarctic; The Antarctic; Antarc*
• Online Access: https://elib.dlr.de/136951/; https://ieeexplore.ieee.org/document/9383809

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 data set of Sentinel-1 imagery covering parts of the Antarctic coast, where coastline detection is notoriously difficult. An implementation of our method is available at https://github.com/khdlr/HED-UNet.