Road centerlines extraction from high resolution images based on an improved directional segmentation and road probability
Road centerlines extraction from high resolution images based on an improved directional segmentation and road probability
复制标题
基于改进的方向分割和道路概率从高分辨率图像中提取道路中心线
DOI：
10.1016/j.neucom.2016.03.095
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发表时间：
2016-11
期刊：
影响因子：
6
通讯作者：
Xu Pengfei
中科院分区：
文献类型：
--
作者：
Liu Ruyi;Song Jianfeng;Miao Qiguang;Xue Qing;Xu Pengfei
从高分辨率遥感影像中提取准确的道路网对于交通数据库更新等应用具有重要意义。然而，现有的方法并不能得到令人满意的结果。提出了一种基于剪切变换、方向分割、道路概率、形状特征和灰度化算法的改进的遥感图像道路网提取方法。所提出的方法包括以下步骤。首先，将联合收割机剪切变换与方向分割相结合，得到初始道路区域。其次，基于马氏距离和阈值的道路地图与初始道路区域融合，以提高准确性。第三，利用道路形状特征滤波和空洞填充提取可靠路段。最后，采用基于快速行进的自动亚体素精确分割方法提取道路中心线。然后通过后处理生成道路网络。实验结果表明，该方法能准确、平滑地提取道路中心线。
It is very important to extract accurate road networks from high resolution remote sensing images for various applications, such as transportation database updating. However, existing approaches cannot get satisfactory results. We propose an improved road networks extraction from remote sensing images based on the shear transform, the directional segmentation, the road probability, shape features and a skeletonization algorithm. The proposed method includes the following steps. First, we combine shear transform with directional segmentation to get the initial road regions. Second, road map based on Mahalanobis distance and thresholding is fused with the initial road regions to improve accuracy. Third, road shape features filtering and hole filling are used to extract reliable road segments. Finally, the road centerlines are extracted by an automatic subvoxel precise skeletonization method based on fast marching. Road networks are then generated by post-processing. Experimental results show that the proposed method can extract smooth and correct road centerlines.
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影响因子：
2.5
作者：
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通讯作者：
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影响因子：
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作者：
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DOI：
10.1109/jstars.2012.2199085
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影响因子：
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