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Toward Accurate and Efficient Road Extraction by Leveraging the Characteristics of Road Shapes

文献类型：期刊论文

作者Wang, Changwei1,3; Xu, Rongtao1,3; Xu, Shibiao1,3; Meng, Weiliang1,3; Wang, Ruisheng2; Zhang, Jiguang1,3; Zhang, Xiaopeng1,3
刊名IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
出版日期2023
卷号61页码:16
关键词Efficient and accurate road extraction efficient strip transformer module (ESTM) geometric deformation estimation module (GDEM) road edge focal loss (REF loss) road shape-aware network (RSANet)
ISSN号0196-2892
DOI10.1109/TGRS.2023.3284478
通讯作者Xu, Shibiao(shibiaoxu@bupt.edu.cn) ; Meng, Weiliang(weiliang.meng@ia.ac.cn)
英文摘要Automatically extracting roads from very high-resolution (VHR) remote sensing images is of great importance in a wide range of remote sensing applications. However, complex shapes of roads (i.e., long, geometrically deformed, and thin) always affected the extraction accuracy, which is one of the challenges of road extraction. Based on the insight into road shape characteristics, we propose a novel road shape-aware network (RSANet) to achieve efficient and accurate road extraction. First, we introduce the efficient strip transformer module (ESTM) to efficiently capture the global context to model the long-distance dependence required by long roads. Second, we design a geometric deformation estimation module (GDEM) to adaptively extract the context from the shape deformation caused by shooting roads from different perspectives. Third, we provide a simple but effective road edge focal loss (REF loss) to make the network focus on optimizing the pixels around the road to alleviate the unbalanced distribution of foreground and background pixels caused by the roads being too thin. Finally, we conduct extensive evaluations on public datasets to verify the effectiveness of RSANet and each of the proposed components. Experiments validate that our RSANet outperforms state-of-the-art methods for road extraction in remote sensing images.
WOS关键词CENTERLINE EXTRACTION ; NETWORK ; INFORMATION ; FEATURES ; IMAGERY ; AWARE
资助项目National Natural Science Foundation of China[U21A20515] ; National Natural Science Foundation of China[62271074] ; National Natural Science Foundation of China[U2003109] ; National Natural Science Foundation of China[62171321] ; National Natural Science Foundation of China[62071157] ; National Natural Science Foundation of China[62162044] ; National Natural Science Foundation of China[61971418] ; Open Research Fund of Key Laboratory of Space Utilization, Chinese Academy of Sciences[LSU-KFJJ-2021-05] ; Open Projects Program of National Laboratory of Pattern Recognition
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001017380100015
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China ; Open Research Fund of Key Laboratory of Space Utilization, Chinese Academy of Sciences ; Open Projects Program of National Laboratory of Pattern Recognition
源URL[http://ir.ia.ac.cn/handle/173211/53795]  
专题多模态人工智能系统全国重点实验室
通讯作者Xu, Shibiao; Meng, Weiliang
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Univ Calgary, Dept Geomat Engn, Calgary, AB T2N 1N4, Canada
3.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Changwei,Xu, Rongtao,Xu, Shibiao,et al. Toward Accurate and Efficient Road Extraction by Leveraging the Characteristics of Road Shapes[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2023,61:16.
APA Wang, Changwei.,Xu, Rongtao.,Xu, Shibiao.,Meng, Weiliang.,Wang, Ruisheng.,...&Zhang, Xiaopeng.(2023).Toward Accurate and Efficient Road Extraction by Leveraging the Characteristics of Road Shapes.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,61,16.
MLA Wang, Changwei,et al."Toward Accurate and Efficient Road Extraction by Leveraging the Characteristics of Road Shapes".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 61(2023):16.

入库方式： OAI收割

来源：自动化研究所

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