Main Road Extraction from ZY-3 Grayscale Imagery Based on Directional Mathematical Morphology and VGI Prior Knowledge in Urban Areas.
Main Road Extraction from ZY-3 Grayscale Imagery Based on Directional Mathematical Morphology and VGI Prior Knowledge in Urban Areas.
复制标题
基于定向数学形态学和VGI先验知识的ZY-3灰度图像主干道提取
DOI：
10.1371/journal.pone.0138071
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Liu W
中科院分区：
文献类型：
--
作者：
Liu B;Wu H;Wang Y;Liu W
从遥感图像中提取的主要道路特征在许多民用和军事应用中发挥着重要作用，例如更新地理信息系统（GIS）数据库，城市结构分析，空间数据匹配和道路导航。目前，从高分辨率图像中提取道路特征的方法通常是基于阈值分割。然而，很难将道路特征与背景完全分离。我们提出了一种新的方法提取主要道路从高分辨率的灰度图像的基础上的方向数学形态学和先验知识，从OpenStreetMap中发现的嵌入式地理信息。该方法的两个主要步骤是：（1）利用方向数学形态学增强道路与非道路的对比度;（2）利用OpenStreetMap道路作为先验知识对遥感图像进行分割。在两幅紫苑三号卫星图像和一幅QuickBird高分辨率灰度图像上进行了实验，并与其他常用的道路特征提取方法进行了比较。实验结果表明，该方法在城市主干道特征提取中具有较好的效果。
Main road features extracted from remotely sensed imagery play an important role in many civilian and military applications, such as updating Geographic Information System (GIS) databases, urban structure analysis, spatial data matching and road navigation. Current methods for road feature extraction from high-resolution imagery are typically based on threshold value segmentation. It is difficult however, to completely separate road features from the background. We present a new method for extracting main roads from high-resolution grayscale imagery based on directional mathematical morphology and prior knowledge obtained from the Volunteered Geographic Information found in the OpenStreetMap. The two salient steps in this strategy are: (1) using directional mathematical morphology to enhance the contrast between roads and non-roads; (2) using OpenStreetMap roads as prior knowledge to segment the remotely sensed imagery. Experiments were conducted on two ZiYuan-3 images and one QuickBird high-resolution grayscale image to compare our proposed method to other commonly used techniques for road feature extraction. The results demonstrated the validity and better performance of the proposed method for urban main road feature extraction.