Enhance Essential Features for Road Extraction from Remote Sensing Images

In deep learning based road extraction from remote sensing images, the network often learns some features that are not essential to road discrimination, such as trees, buildings, etc. In fact, there is no causal relationship between these features and road discrimination, which will lead to error and omission in final results. In this paper, we propose a novel road extraction network to enhance essential features, including local and global line features and geometric features along the road direction. Multi-scale Line Enhancement Module utilize hough transform to enhance line featues of different scales. Neighboring road prediction branch make the network pre-dict the distance and direction of each pixel to the neighboring road, which helps the network to focus on geometric features along the road direction. Experimental results on the deepglobe dataset show that the network is able to obtain bet-ter road extraction results by enhancing essential features that have a causal relationship with the task. Codes are available at https://github.com/zaoyifan/EssentialFeatures.