Extraction of road edge lines from remote sensing image based on image blocking and line segment voting

Abstract

Edge lines extraction from remote sensing images is a classic problem,and different edge extraction algorithms are applicable to different types of images. The road shape is not very regular,the contrast is low and the impact of noise is serious in actual remote sensing image because the road might be blocked by buildings and trees,and the road edge lines are likely to be broken; therefore road edge lines extraction from high- resolution remote sensing image is always a hot research topic. In this paper,the authors propose a new method for extraction of the road lines from remote sensing image so as to solve the problem that it is difficult for the methods available to extract clear and continuous road edge lines. Firstly,the direction templates are introduced to detect the edge points and search for the sub- segments in block image; then the sub- segments are extended and the line segment voting is taken to connect straight line segments in the curved edge lines,and the edge lines whose length is greater than a given threshold are output; finally,the spur and bifurcation are removed and the union of edge lines in eight directions is taken as the final road network. Experiment results show that the method proposed in this paper can be used to extract the road edge lines which have a certain curvature and low contrast and are affected by noise seriously from high- resolution remote sensing images.