A template-matching based approach for extraction of roads from very high-resolution remotely sensed imagery

Automated or semi-automated extraction of road networks is a prerequisite to fast acquisition and update of geospatial data. Actually, line-shaped lane markings and/or median strips on road surfaces are less impacted by occlusions of vehicles or shadows of trees than the other parts of road surfaces on very highresolution (VHR) remotely sensed imagery. These features provide a clue for road extraction. This article proposes an approach for semi-automated extraction of road networks by tracking apparent lane markings and/or median strips on VHR imagery. After preprocessing the raw image, three seed points on a short road segment are manually selected, which indicate starting point, direction and width of the road, respectively. Based on the manually selected data, a reference template of the road, which is composed of two components: a cross-section profile, rectangular templates of lane markings and median strips, is created. With the created reference template, automated road tracking is triggered. During the process of road tracking, a least-squares template matching is employed to search the optimal road centreline points, and a human operator is retained in the loop to guide the computer. The above operation is repeated until an entire road network is completely extracted. Tests of the above-proposed method are conducted on both aerial and VHR satellite imagery. The results show that the proposed method can successfully track over 94% of the highways and 81% of the arterial roads from the VHR images, and save the time of 26% when comparing to traditional methods.