Semantic segmentation of remote sensing imagery for road extraction via joint angle prediction: comparisons to deep learning - Xiong, Shun; Ma, Chao; Yang, Guang; Song, Yaodong; Liang, Shuaizhe; Feng, Jing (Frontiers in Earth Science, 2023)
The accurate road network information is required to study and analyze the relationship between land usage type and land subsidence, and road extraction from remote sensing images is an important data source for updating road network. This task has been considered as a significant semantic segmentation problem, given many road extraction methods developed for remote sensing images in recent year. Although impressive results have been achieved by classifying each pixel in the remote sensing image using semantic segmentation network, traditional semantic segmentation methods often lacks clear constraints of road feature. Consequently, the geometric features of the results might deviate from actual roads, leading to issues like road fractures, rough edges, inconsistent road widths, and more, which hinder their effectiveness in road update. This paper proposes a novel road semantic segmentation algorithm for remote sensing images based on the joint road angle prediction. By incorporating the angle prediction module and the angle feature fusion module, constraints are added to the angle features of road. Through the angle prediction and angle feature fusion, the information contained in the remote sensing images can be better utilized. The experimental results show that the proposed method outperforms existing semantic segmentation methods in both quantitative evaluation and visual effects. Furthermore, the extracted roads were consecutive with distinct edges, making them more suitable for map road updates.