Joint Superpixel Segmentation and Graph Convolutional Network Road Extration for High-Resolution Remote Sensing Imagery

Extracting roads from remote sensing images has both civilian and military value, such as GIS data update, road navigation, military command and so on. The existing road extraction methods are mainly based on fully convolutional neural networks, and have achieved the state-of-the-art results. However, the convolutional and deconvolutional forms of these methods destroy the completeness of the extracted road. In this paper, we present a novel road extraction method for extracting complete roads from high-resolution remote sensing imagery based on joint superpixel segmentation and Graph Convolutional Network(GCN). The proposed method retains more spatial detail information as well as effectively improves the integrity of the extracted roads. Experiments were conducted on the Massachusetts Road dataset to compare our proposed method to other commonly used full convolutional techniques for road extraction. The results demonstrated the validity and better performance of the proposed method.