Boundary Loss for Remote Sensing Imagery Semantic Segmentation
This work addresses the need for precise boundary extraction in remote sensing imagery, which is crucial for applications like geospatial analysis, but it is incremental as it builds upon existing segmentation methods.
The authors tackled the problem of inaccurate boundary delineation in remote sensing image segmentation by proposing a novel loss function, which improved models' performance over baselines in terms of IoU score on synthetic and real-world datasets.
In response to the growing importance of geospatial data, its analysis including semantic segmentation becomes an increasingly popular task in computer vision today. Convolutional neural networks are powerful visual models that yield hierarchies of features and practitioners widely use them to process remote sensing data. When performing remote sensing image segmentation, multiple instances of one class with precisely defined boundaries are often the case, and it is crucial to extract those boundaries accurately. The accuracy of segments boundaries delineation influences the quality of the whole segmented areas explicitly. However, widely-used segmentation loss functions such as BCE, IoU loss or Dice loss do not penalize misalignment of boundaries sufficiently. In this paper, we propose a novel loss function, namely a differentiable surrogate of a metric accounting accuracy of boundary detection. We can use the loss function with any neural network for binary segmentation. We performed validation of our loss function with various modifications of UNet on a synthetic dataset, as well as using real-world data (ISPRS Potsdam, INRIA AIL). Trained with the proposed loss function, models outperform baseline methods in terms of IoU score.