Road network extraction and intersection detection from aerial images by tracking road footprints
Road network extraction and intersection detection from aerial images by tracking road footprints
复制标题
DOI：
10.1109/tgrs.2007.906107
复制
发表时间：
2007-12-01
影响因子：
8.2
通讯作者：
Wonka, Peter
中科院分区：
文献类型：
--
作者：
Hu, Jiuxiang;Razdan, Anshuman;Wonka, Peter
本文提出了一种从航空影像中自动提取道路网的两步法(检测和剪枝)。道路检测步骤基于像素周围的局部均匀区域的形状分类。局部均匀区域被称为像素轮廓线的多边形包围。这一步包括检测道路足迹、跟踪道路和种植行道树。我们使用轮辐操作员来获取道路足迹。我们提出了一种基于道路足迹矩形近似的自动道路播种方法和一种用于种植道路树的足迹分类的脚趾查找算法。道路树修剪步骤利用基于足迹面积周长比(A/P比)的贝叶斯决策模型来修剪泄漏到周围环境的路径。我们引入对数正态分布来刻画道路树足迹A/P比的条件概率，并提出了一种自动估计与贝叶斯决策模型相关的参数的方法。给出了各种航空图像的结果。利用典型航拍图像对提取的道路网络进行评价，结果表明，道路跟踪器的完备率在84%~94%之间，正确率在81%以上，质量在82%~92%之间。
In this paper, a new two-step approach (detecting and pruning) for automatic extraction of road networks from aerial images is presented. The road detection step is based on shape classification of a local homogeneous region around a pixel. The local homogeneous region is enclosed by a polygon, called the footprint of the pixel. This step involves detecting road footprints, tracking roads, and growing a road tree. We use a spoke wheel operator to obtain the road footprint. We propose an automatic road seeding method based on rectangular approximations to road footprints and a toe-finding algorithm to classify footprints for growing a road tree. The road tree pruning step makes use of a Bayes decision model based on the area-to-perimeter ratio (the A/P ratio) of the footprint to prune the paths that leak into the surroundings. We introduce a lognormal distribution to characterize the conditional probability of A/P ratios of the footprints in the road tree and present an automatic method to estimate the parameters that are related to the Bayes decision model. Results are presented for various aerial images. Evaluation of the extracted road networks using representative aerial images shows that the completeness of our road tracker ranges from 84% to 94%, correctness is above 81%, and quality is from 82% to 92%.