Generating Pixel Enhancement for Road Extraction in High-Resolution Aerial Images

Road extraction is a powerful technique support to autonomous driving as it provides routable road information for motion planning algorithms. High-resolution aerial images offer comprehensive road information, facilitating the establishment of efficient and accurate road networks to monitor road changes in a timely manner. However, widespread occlusions and abundant local details pose challenges to highly accurate and continuous extraction, especially in areas with road bifurcations. To simultaneously improve both accuracy and connectivity of road extraction, in this paper, we propose a novel approach TPEGAN to integrate pixel-level segmentation and graph inference based on road pixel enhancement. By generating road pixels enhanced images, the generative adversarial network exploits the consistency among road pixels to embed pixel-level accuracy into the segmentation module. The multi-scale dual-branch segmentation module employs graph convolution reasoning to capture dependencies across different spatial regions, maintaining the connectivity of road networks. Extensive experiments on three public datasets demonstrate that TPEGAN outperforms SOTA methods with a considerable performance gap. As the complexity of road networks increases, the performance of TPEGAN degrades more slowly than SOTA method. Even in challenging urban scenes where the proportion of road pixels is more than 15%, TPEGAN retains its high performance and achieves a rIoU of 0.664 with an APLS of 72.81%, amounting to improvements of 4.1% and 2.62% over SOTA method, respectively.