结合测地距离场与曲线平滑的遥感图像道路中心线快速提取

A quick road centreline extraction method from remote sensing images combining with geodesic distance field and curve smoothing

从高分辨遥感图像中快速提取道路信息在地图绘制、城市规划和更新GIS数据库等方面至关重要,半自动道路提取作为道路测绘内业的主要方式,是一项劳动密集型工作。为了降低人工介入代价,提高工作效率,本文提出了一种基于测地距离场的道路中心线快速提取算法。首先,利用最佳圆形模板算法,自动估计道路宽度的同时将人工种子调整到道路中心;然后,为了定位道路中心线,提出基于道路显著图的柔性道路中心核密度估计算法,克服了传统道路中心核密度估计中道路分割阈值预设困难的问题;本文提出快速生成测地距离场算法,可快速跟踪种子之间的测地线,大大提高了道路中心线提取的效率;最后对测地线坐标进行均值滤波平滑,获得了光滑的道路中心线。大量的试验和对比数据表明,本文算法能够在保证精度的前提下快速提取道路中心线,大幅降低人工介入代价,提高道路提取的工作效率;值得强调的是,本文算法在图像分辨率固定的前提下,提取任意长度道路中心线的耗时近乎相同,且无须设置超参数,具有较好的人机交互体验。

Quickly extracting road networks from high-resolution remote sensing images is crucial in mapping,urban planning,and GIS databases updating.Semi-automatic road extraction,as the main method of road surveying and mapping,is a labor-intensive task.In order to reduce the cost of manual intervention and improve extraction efficiency,this paper proposes a fast road centerline extraction algorithm based on geodesic distance field.First,the optimal circular template is proposed to automatically estimated the road width and adjust the manual seeds to road center based on the morphological gradient map,and the road saliency map is calculated according to the local color features inside the templates.Second,we propose the soft road center kernel density based on road saliency map which overcomes the difficulty of threshold presetting of road segmentation in traditional road center kernel density estimation.Most importantly,a geodesic distance field is proposed to quickly extract the geodesic curve between two consecutive seeds,which dramatically increase the efficiency of our algorithm.Finally,we introduce the mean filter into our scheme to smooth the road centerlines.Extensive experiments and quantitative comparisons show that the proposed algorithm can greatly reduce manual intervention without losing much accuracy,and significantly improve the efficiency of road extraction.Furthermore,the proposed algorithm takes almost the same time to extract any length of road centerline given fixed image size,and no hyperparameters need to be set.The algorithm behaves good experience in human-computer interaction.