Road Centerline Extraction via Semisupervised Segmentation and Multidirection Nonmaximum Suppression

doi:10.1109/LGRS.2016.2524025

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 先进时空数据分析与学习

	Road Centerline Extraction via Semisupervised Segmentation and Multidirection Nonmaximum Suppression
	Cheng, Guangliang; Zhu, Feiyun; Xiang, Shiming; Pan, Chunhong; Cheng GL(程光亮)
发表期刊	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
	2016-04-01
卷号	13 期号:4 页码:545-549
文章类型	Article
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.
关键词	Multidirection Nonmaximum Suppression (M-nms) Multiscale Filtering (Mf) Road Centerline Extraction Semisupervised Segmentation
WOS标题词	Science & Technology ; Physical Sciences ; Technology
DOI	10.1109/LGRS.2016.2524025
关键词[WOS]	SHAPE-FEATURES ; IMAGERY ; CLASSIFICATION
收录类别	SCI
语种	英语
项目资助者	Natural Science Foundation of China(91338202 ; 91438105 ; 61305049 ; 61370039)
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:000373009800015
引用统计	被引频次：46[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/173211/12189
专题	多模态人工智能系统全国重点实验室_先进时空数据分析与学习
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
第一作者单位	模式识别国家重点实验室
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.

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