Many road segmentation methods based on CNNs have been proposed for remote sensing images in recent years. Although these techniques show great performance in various applications, there are still problems in road segmentation, due to the existence of complex backgrounds, illumination changes, and occlusions due to trees and cars. In this paper, we propose a two-stage strategy for road segmentation. A probability map is generated in the first stage by a selected network (ResUnet is used as a case study in this paper), then we attach the probability map image to the original RGB images and feed the resulting four images to a U-Net-like network in the second stage to get a refined result. Our experiments on the Massachusetts road dataset show the average IoU can increase up to 3% from stage one to stage two, which achieves state-of-the-art results on this dataset. Moreover, from the qualitative results, obvious improvements from stage one to stage two can be seen: fewer false positives and better connection of road lines.