Road network extraction from high resolution satellite images

被引:0
作者
Li Gang [1 ,2 ]
Lai Shunnan [3 ]
Li Sheng [3 ,2 ]
机构
[1] Peking University Shenzhen Graduate School
[2] Beijing Engineering Technology Research Center of Virtual Simulation and Visualization
[3] School of Electronics Engineering and Computer Science, Peking University
基金
中国国家自然科学基金;
关键词
road extraction; topology; mathematical morphology; skeletonization; support vector machine;
D O I
10.19583/j.1003-4951.2016.02.001
中图分类号
TP751 [图像处理方法];
学科分类号
081002 ;
摘要
In this paper, an approach of roads network extraction from high resolution satellite images is presented. First, the approach extracts road surface from satellite image using one-class support vector machine(SVM). Second, the road topology is built from the road surface. The last output of the approach is a series of road segments which is represented by a sequence of points as well as the topological relations among them. The approach includes four steps. In the first step one-class support vector machine is used for classifying pixel of the satellite images to road class or non-road class. In the second step filling holes and connecting gaps for the SVM’s classification result is applied through mathematical morphology close operation. In the third step the road segment is extracted by a series of operations which include skeletonization, thin, branch pruning and road segmentation. In the last step a geometrical adjustment process is applied through analyzing the road segment curvature. The experiment results demonstrate its robustness and viability on extracting road network from high resolution satellite images.
引用
收藏
页码:1 / 7
页数:7
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