A Distance Transform Based Loss Function for the Semantic Segmentation of Very High Resolution Remote Sensing Images

Accurately segmenting region boundaries in complex and high resolution remote sensing scenes, with often a large number of relatively small structures and objects remains a challenge; since conventional loss functions such as Cross-Entropy and Intersection-over-Union often neglect boundary precision and focus instead on the alignment of the entire estimated region. This paper presents a new distance transform-based loss function designed especially to focus on boundary quality enhancement. It is validated with the ISPRS very high spatial resolution Vaihingen and Potsdam remote sensing datasets using a U-Net model with a ResNet-50 encoder. Preliminary results show that the proposed loss function outperforms widely used loss functions across multiple evaluation metrics.