HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline (2103.01849v1)

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 \url{https://github.com/khdlr/HED-UNet}.

• The paper presents HED-UNet, which combines semantic segmentation and edge detection to precisely delineate the dynamic Antarctic coastline.
• It employs an encoder-decoder architecture with deep supervision and a hierarchical attention mechanism for effective multiscale feature extraction.
• Evaluation on Sentinel-1 images shows improved mIoU and F1 scores over traditional methods, underscoring its potential in environmental monitoring.

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

The paper "HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline" presents a novel deep learning-based approach to the challenging task of coastline detection in Antarctica. The research introduces the HED-UNet model, which amalgamates techniques from semantic segmentation and edge detection to more accurately delineate the dynamic and complex Antarctic coastlines using Sentinel-1 satellite imagery.

Methodological Innovations

The HED-UNet model is inspired by the architectures of UNet for semantic segmentation and Holistically-Nested Edge Detection (HED) for edge delineation. The authors recognize the dual nature of coastline detection, where both "area" (segmentation) and "edge" (delineation) information are crucial. The proposed model integrates:

1. Encoder-Decoder Architecture: The model leverages the contextual aggregation strengths of the encoder-decoder architecture. This approach is extended to a feature pyramid network structure, allowing effective handling of multiscale features.
2. Deep Supervision: The model incorporates deep supervision by enforcing predictive tasks at multiple resolutions, aiding the network in encoding informative features across all layers and improving its learning efficiency and generalization ability.
3. Hierarchical Attention Mechanism: A significant contribution of the paper is the introduction of a hierarchical attention mechanism that adaptively merges predictions across different scales. This allows the network to focus on detailed predictions near the coastline while utilizing more general low-resolution features in less critical areas.

Evaluation and Results

The model is rigorously tested on a dataset of Sentinel-1 images covering various regions of the Antarctic coast. It is benchmarked against traditional statistical methods, edge operation techniques, active contour models, and existing deep learning methods like UNet and HRNet with Object-Contextual Representations. Key findings include:

• HED-UNet outperforms existing models in terms of segmentation accuracy and edge delineation, significantly reducing the deviation from the true coastline in challenging areas like the Antarctic Peninsula.
• It shows a notable improvement in the mean Intersection-over-Union (mIoU) and F1 scores for edge detection over other methods, demonstrating its superior ability to handle the complexity of the Antarctic coastal dynamics.
• The inclusion of DEM (Digital Elevation Model) data is explored, revealing enhanced performance in certain cases, albeit with a risk of overfitting.

Implications and Future Directions

The implications of HED-UNet extend beyond simply improving coastline detection. Its architecture points to a potential wider application in tasks requiring simultaneous segmentation and edge detection, such as urban mapping and geological feature delineation. The research suggests that further refinements in efficiently integrating additional data (e.g., DEM) could yield even better performance across diverse Earth observation tasks.

Future developments should explore the adaptability of HED-UNet to other types of remote sensing data and settings. Extending the feature set with improved contextual cues, optimizing the attention mechanisms, and enhancing model scalability are potential areas of investigation. The release of the implementation allows other researchers to build upon this work, potentially enhancing practical applications in environmental monitoring and climate science.

In summary, HED-UNet represents a significant step forward in the automated analysis of complex coastal regions, providing a robust framework that balances detailed segmentation with precise edge detection. Its contributions align with the growing need for accurate monitoring systems in the context of rapid environmental changes affecting polar regions.