Road Centerline Extraction via Semisupervised Segmentation and Multidirection Nonmaximum Suppression

doi:10.1109/LGRS.2016.2524025

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 遥感图像处理团队

	Road Centerline Extraction via Semisupervised Segmentation and Multidirection Nonmaximum Suppression
	Cheng, Guangliang; Zhu, Feiyun; Xiang, Shiming; Pan, Chunhong; Cheng GL(程光亮)
刊名	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
	2016-04-01
卷号	13 期号:4 页码:545-549
关键词	Multidirection Nonmaximum Suppression (M-nms) Multiscale Filtering (Mf) Road Centerline Extraction Semisupervised Segmentation
DOI	10.1109/LGRS.2016.2524025
文献子类	Article
英文摘要	Accurate road centerline extraction from remotely sensed images plays a significant role in road map generation and updating. In the road extraction problem, the acquisition of labeled data is time consuming and costly; thus, there are only a small amount of labeled samples in reality. In the existing centerline extraction algorithms, the thinning-based algorithms always produce small spurs that reduce the smoothness and accuracy of the road centerline; the regression-based algorithms can extract a smooth road network, but they are time consuming. To solve the aforementioned problems, we propose a novel road centerline extraction method, which is constructed based on semi-supervised segmentation and multiscale filtering (MF) and multidirection nonmaximum suppression (M-NMS) (MF&M-NMS). Specifically, a semisupervised method, which explores the intrinsic structures between the labeled samples and the unlabeled ones, is introduced to obtain the segmentation result. Then, a novel MF&M-NMS-based algorithm is proposed to gain a smooth and complete road centerline network. Experimental results on a public data set demonstrate that the proposed method achieves comparable or better performances by comparing with the stateof-the-art methods. In addition, our method is nearly ten times faster than the state-of-the-art methods.
WOS关键词	SHAPE-FEATURES ; IMAGERY ; CLASSIFICATION
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
WOS记录号	WOS:000373009800015
资助机构	Natural Science Foundation of China(91338202 ; 91438105 ; 61305049 ; 61370039)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/12189]
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	Cheng GL(程光亮)
作者单位	Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Cheng, Guangliang,Zhu, Feiyun,Xiang, Shiming,et al. Road Centerline Extraction via Semisupervised Segmentation and Multidirection Nonmaximum Suppression[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2016,13(4):545-549.
APA	Cheng, Guangliang,Zhu, Feiyun,Xiang, Shiming,Pan, Chunhong,&程光亮.(2016).Road Centerline Extraction via Semisupervised Segmentation and Multidirection Nonmaximum Suppression.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,13(4),545-549.
MLA	Cheng, Guangliang,et al."Road Centerline Extraction via Semisupervised Segmentation and Multidirection Nonmaximum Suppression".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 13.4(2016):545-549.