像元与对象特征融合的高分辨率遥感影像道路中心线提取

曹云刚; 王志盼; 慎利; 肖雪; 杨磊

doi:10.11947/j.AGCS.2016.20160158

测绘学报 >

2016 , Vol. 45 >Issue 10: 1231 - 1240

DOI: https://doi.org/10.11947/j.AGCS.2016.20160158

摄影测量学与遥感

像元与对象特征融合的高分辨率遥感影像道路中心线提取

曹云刚 ,
王志盼 ,
慎利 ,
肖雪 ,
杨磊

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1. 西南交通大学高速铁路运营安全空间信息技术国家地方联合工程实验室, 四川成都 611756;
2. 西南交通大学地球科学与环境工程学院, 四川成都 611756;
3. 四川省第二测绘地理信息工程院, 四川成都 610100

曹云刚(1978-),男,副教授,研究方向为资源与环境遥感。E-mail:yungang@swjtu.cn

收稿日期: 2016-04-07

修回日期: 2016-06-16

网络出版日期: 2016-11-08

基金资助

国家重点基础研究计划（2012CB719901）；国家自然科学基金（41201434；41401374）；数字制图与国土信息应用工程国家测绘地理信息局重点实验室开放基金（DM2016SC06）；四川省地理国情监测工程技术研究中心开放基金（GC201516）

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Fusion of Pixel-based and Object-based Features for Road Centerline Extraction from High-resolution Satellite Imagery

CAO Yungang ,
WANG Zhipan ,
SHEN Li ,
XIAO Xue ,
YANG Lei

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1. State-province Joint Engineering Laboratory of Spatial Information Technology of High-speed Railway Safety, Southwest Jiaotong University, Chengdu 611756, China;
2. Faculty of Geosciences and Environmental Engineering Southwest Jiaotong University, Chengdu 611756, China;
3. Sichuan Province Second Geographic Information Engineering Institute of Surveying and Mapping, Chengdu 610100, China

Received date: 2016-04-07

Revised date: 2016-06-16

Online published: 2016-11-08

Supported by

The National Basic Research Program of China(973 Program) (No.2012CB719901);The National Natural Science Foundation of China (Nos. 41201434;41401374);The Key Laboratory of Digital Mapping and Land Information Application of National Administration of Surveying, Mapping and Geoinformation(No.DM2016SC06);Geographical Condition Monitoring Engineering Technology Research Center of Sichuan Province((No.GC201516))

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摘要

提出了一种融合像元-多尺度对象级特征的高分辨率遥感影像道路中心线提取方法。首先在像素级上提取影像的纹理和形状结构特征，在构建的多尺度分割集影像上提取对象的区域光谱特征。然后，将像元级特征与多尺度对象特征进行决策级融合，完成道路网的粗提取。最后，结合本文所提出的非道路区域自动去除算法和张量投票算法，实现道路中心线的精提取。不同场景、不同分辨率数据下开展的试验结果表明，该方法可有效改善传统道路提取方法易产生的“盐噪声”和非道路地物粘连现象。

关键词： 高分辨率遥感; 多特征融合; 道路提取; 基于像素; 面向对象

本文引用格式

曹云刚 , 王志盼 , 慎利 , 肖雪 , 杨磊 . 像元与对象特征融合的高分辨率遥感影像道路中心线提取[J]. 测绘学报, 2016 , 45(10) : 1231 -1240 . DOI: 10.11947/j.AGCS.2016.20160158

Abstract

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.

Key words： high resolution remote sensing; multiple feature fusion; road extraction; pixel-based; object-based

参考文献

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