Automatic Road Detection and Centerline Extraction via Cascaded End-to-End Convolutional Neural Network
文献类型：期刊论文
| 作者 | Cheng, Guangliang ; Wang, Ying; Xu, Shibiao; Wang, Hongzhen; Xiang, Shiming; Pan, Chunhong
|
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2017-03-07 |
| 卷号 | 55期号:6页码:3322-3337 |
| 关键词 | Cascaded Convolutional Neural Network (Casnet) End-to-end Road Centerline Extraction Road Detection |
| DOI | 10.1109/TGRS.2017.2669341 |
| 文献子类 | Article |
| 英文摘要 | 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. |
| WOS关键词 | REMOTE-SENSING IMAGERY ; SCENE CLASSIFICATION ; SHAPE-FEATURES ; SEGMENTATION ; SYSTEM |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000402063500021 |
| 资助机构 | National Natural Science Foundation of China(91646207 ; Beijing Natural Science Foundation(4162064) ; 91338202 ; 61620106003 ; 61305049) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/14535]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_遥感图像处理团队 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。