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

Code

• github.com/khdlr/HED-UNet
  OfficialIn paperpytorch★ 112
• github.com/code-implementation1/Code4/tree/main/hed
  mindspore★ 0
• github.com/MindSpore-paper-code-3/code4/tree/main/hed
  mindspore★ 0
• github.com/2023-MindSpore-1/ms-code-214/tree/main/hed
  mindspore★ 0
• github.com/2023-MindSpore-4/Code-5/tree/main/hed
  mindspore★ 0

Abstract

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 https://github.com/khdlr/HED-UNet.

Tasks

Reproductions