中文摘要：

目的利用分类算法对高分辨率影像中的道路进行分割时，得到的二值图像往往混杂了许多非道路区域，且道路区域呈面状，无法直接应用于生产与研究。针对该问题，提出一种利用邻域质心投票提取道路中心线的算法。方法首先检测像素在各方向上的连通距离以构建邻域多边形，随后进行质心投票来提取道路的中心线，与此同时估算道路宽度并判断出连通距离较长的方向数目，以排除非道路区域的干扰，最后经形态学处理得到细化的中心线。结果选取测试图像及具有不同道路分布特征的高分辨率航空影像的分类结果进行实验，并将该算法与Zhang和Couloigner提出的算法进行了对比分析。结果显示，该算法的提取质量为80．6％和79．0％，且计算效率较高，处理实际影像的用时小于参考算法的20％，此外在稳定性及对不同路宽的适应性等多个方面均具有优势。结论提出一种邻域质心投票算法，该算法能够同时实现传统方法中提纯与中心线提取两个步骤所对应的功能，从分类影像直接提取道路中心线。实验结果表明，该算法能够根据形状特征有效检测道路，且具备一定抗干扰能力，适用于对混杂了非道路区域的高分辨率影像的分类结果进行处理。

英文摘要：

Objective 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. Method 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. Result 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. Conclusion 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 together.