高分辨率遥感影像中城市道路网的提取方法研究

项目名称： 高分辨率遥感影像中城市道路网的提取方法研究

项目编号： No.41271420

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 天文学、地球科学

项目作者： 周绍光

作者单位： 河海大学

项目金额： 75万元

中文摘要： 现有的道路提取和图像处理理论中，存在着一些可部分解决道路提取不同方面问题的有效思想，可惜少有人对其进行总结和归纳。由前期研究得知：从一幅普通的高分辨率影像中提取城市道路网必然涉及到道路点分割、道路段形成和修整、道路段连接和道路矢量化几个环节，其中最关键的步骤是道路分割。本项目试图综合运用马尔科夫随机场、混合模型、形状先验以及轨迹跟踪等理论和技术将道路从背景中逐条分割出来。分割所用的特征值包括原始的多光谱数据和道路纹理信息。申请者的前期研究表明：利用随机场理论和形状先验知识可获取连续、光滑的道路；而借鉴轨迹跟踪思想可依据已获取道路段指导后续路面的分割，并能填补断裂。利用混合模型描述路面和背景的概率密度是本项目的难点。此路面分割方案综合了几种理论的优点，可应用于道路的自动和半自动提取，还可结合已有道路网信息，检测旧道路在新影像中是否存在，整个项目的实施对提高城市道路提取的水平将产生积极影响。

中文关键词： 道路提取；机器学习；遥感图像处理；高斯混合模型；形状先验

英文摘要： In the large amount of related papers, there are some methods which are partially effective for road extraction. Combining them may help us to find a new way for extracting roads more accurately and more efficiently. To extract road networks in urban area from high resolution remotely sensed images will inevitably involve solving the following problems: segmenting roads, generating road segments, smoothing road segments, linking road segments and vectorizing roads. The most critical problem is segmenting roads. Ideal segmentation results output continuous and smooth road ribbons which contain no holes and are not attached by irregular blocks, meanwhile roads are segmented sequentially. All these mean that no post-segmentation processes are needed, because the segmentation results are the very vectorized road networks we want. We hope a new segmentation method can segment roads as ideal as possible. In this project we attempt to segment roads from the background one by one, by the integrated use of Markov random field theory, mixture model theory, target tracking theory and shape prior technique. Multispectral data and texture values consist of the feature vector .Our previous studies showed that smooth roads may be obtained in the segmentation step with the aid of Markov random field theory and shape prior knowl

英文关键词： road extraction；machine learning；remote sensing image processing；Gaussian mixture model；shape prior