When applying image classification algorithms on high-resolution images to extract roads

non-road areas do exist in the binary result. Meanwhile

the achieved roads are planar

which cannot be used directly for production and research purposes. In this case

a novel algorithm named neighborhood centroid voting is proposed to extract road centerlines. First

a neighborhood polygon for each road pixel is built by detecting the connective distance in each direction. Then

centroids of these polygons are voted for to extract road centerlines. At the same time

road width is estimated and the number of those directions

comparatively long connective distance is recorded to exclude non-road areas. Finally

morphological methods are applied to obtain thinned centerlines. A comparison is made between this algorithm and a reference method proposed by Zhang and Couloigner via experiments on a test image and two classified high-resolution aerial images with different road distributions. Results suggest that the quality of this algorithm for the respective two images is 80.6% and 79.0%. Taking less than 20% of the time of the reference method for dealing with actual images

this algorithm has a strong advantage because of its effectiveness. Additionally

this algorithm is more stable and can adapt to roads with varying widths. The proposed algorithm named neighborhood centroid voting is a centerline extraction algorithm capable of doing work corresponding to road refinement and centerline extraction in a conventional approach at the same time. Experimental findings suggest that this algorithm can detect roads effectively according to shape features

with resistance to disturbances

applicable to high-resolution classified images with roads and non-road areas mixed toge-ther.