Fusion of pixel-based and object-based features for road centerline extraction from high-resolution satellite imagery

被引：0
|
作者：
Cao Y. [1 ,2 ]
Wang Z. [1 ,2 ]
Shen L. [1 ,2 ]
Xiao X. [1 ,2 ]
Yang L. [3 ]
机构：
[1] State-province Joint Engineering Laboratory of Spatial Information Technology of High-speed Railway Safety, Southwest Jiaotong University, Chengdu
[2] Faculty of Geosciences and Environmental Engineering Southwest Jiaotong University, Chengdu
[3] Sichuan Province Second Geographic Information Engineering Institute of Surveying and Mapping, Chengdu
来源：
Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | 2016年 / 45卷 / 10期
基金：
中国国家自然科学基金;
关键词：
High resolution remote sensing; Multiple feature fusion; Object-based; Pixel-based; Road extraction;
D O I：
10.11947/j.AGCS.2016.20160158
中图分类号：
学科分类号：
摘要：
A novel approach for road centerline extraction from high spatial resolution satellite imagery is proposed by fusing both pixel-based and object-based features. Firstly, texture and shape features are extracted at the pixel level, and spectral features are extracted at the object level based on multi-scale image segmentation maps. Then, extracted multiple features are utilized in the fusion framework of Dempster-Shafer evidence theory to roughly identify the road network regions. Finally, an automatic noise removing algorithm combined with the tensor voting strategy is presented to accurately extract the road centerline. Experimental results using high-resolution satellite imageries with different scenes and spatial resolutions showed that the proposed approach compared favorably with the traditional methods, particularly in the aspect of eliminating the salt noise and conglutination phenomenon. © 2016, Surveying and Mapping Press. All right reserved.
引用
收藏
页码：1231 / 1240and1249
相关论文
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