Semantic segmentation of remote sensing imagery for road extraction via joint angle prediction: comparisons to deep learning

Abstract

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 networks. This task has been considered a significant semantic segmentation problem, given the many road extraction methods developed for remote sensing images in recent years. Although impressive results have been achieved by classifying each pixel in the remote sensing image using a semantic segmentation network, traditional semantic segmentation methods often lack clear constraints of road features. 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 updates. 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 the 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 mapping road updates.