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Automatic Road Detection and Centerline Extraction via Cascaded End-to-End Convolutional Neural Network
Cheng, Guangliang; Wang, Ying; Xu, Shibiao; Wang, Hongzhen; Xiang, Shiming; Pan, Chunhong
2017-03-07
发表期刊IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷号55期号:6页码:3322-3337
文章类型Article
摘要Accurate road detection and centerline extraction from very high resolution (VHR) remote sensing imagery are of central importance in a wide range of applications. Due to the complex backgrounds and occlusions of trees and cars, most road detection methods bring in the heterogeneous segments; besides for the centerline extraction task, most current approaches fail to extract a wonderful centerline network that appears smooth, complete, as well as single-pixel width. To address the above-mentioned complex issues, we propose a novel deep model, i.e., a cascaded end-to-end convolutional neural network (CasNet), to simultaneously cope with the road detection and centerline extraction tasks. Specifically, CasNet consists of two networks. One aims at the road detection task, whose strong representation ability is well able to tackle the complex backgrounds and occlusions of trees and cars. The other is cascaded to the former one, making full use of the feature maps produced formerly, to obtain the good centerline extraction. Finally, a thinning algorithm is proposed to obtain smooth, complete, and single-pixel width road centerline network. Extensive experiments demonstrate that CasNet outperforms the state-of-the-art methods greatly in learning quality and learning speed. That is, CasNet exceeds the comparing methods by a large margin in quantitative performance, and it is nearly 25 times faster than the comparing methods. Moreover, as another contribution, a large and challenging road centerline data set for the VHR remote sensing image will be publicly available for further studies.
关键词Cascaded Convolutional Neural Network (Casnet) End-to-end Road Centerline Extraction Road Detection
WOS标题词Science & Technology ; Physical Sciences ; Technology
DOI10.1109/TGRS.2017.2669341
关键词[WOS]REMOTE-SENSING IMAGERY ; SCENE CLASSIFICATION ; SHAPE-FEATURES ; SEGMENTATION ; SYSTEM
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(91646207 ; Beijing Natural Science Foundation(4162064) ; 91338202 ; 61620106003 ; 61305049)
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号WOS:000402063500021
引用统计
被引频次:9[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/14535
专题空天信息研究中心
通讯作者Xu, Shibiao
作者单位Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714
Cheng, Guangliang,Wang, Ying,Xu, Shibiao,et al. Automatic Road Detection and Centerline Extraction via Cascaded End-to-End Convolutional Neural Network[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2017,55(6):3322-3337.
APA Cheng, Guangliang,Wang, Ying,Xu, Shibiao,Wang, Hongzhen,Xiang, Shiming,&Pan, Chunhong.(2017).Automatic Road Detection and Centerline Extraction via Cascaded End-to-End Convolutional Neural Network.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,55(6),3322-3337.
MLA Cheng, Guangliang,et al."Automatic Road Detection and Centerline Extraction via Cascaded End-to-End Convolutional Neural Network".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 55.6(2017):3322-3337.
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