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Accurate urban road centerline extraction from VHR imagery via multiscale segmentation and tensor voting
Cheng GL(程光亮); Zhu FY(朱飞云); Xiang SM(向世明); Wang Y(王颖); Pan CH(潘春洪)
Source PublicationNeurocomputing
2016-04
Issue205Pages:407-420
AbstractAccurate road centerline extraction from very-high-resolution (VHR) remote sensing imagery has various applications, such as road map generation and updating etc. There are three shortcomings of existing methods: (a) due to noise and occlusions, most road extraction methods bring in heterogeneous classification results; (b) morphological thinning is a fast and widely used algorithm to extract road centerline, while it produces small spurs; (c) many methods are ineffective to extract centerline around the road intersections. To address the above three issues, we propose a novel road centerline extraction method via three techniques: fused multiscale collaborative representation (FMCR) & graph cuts (GC), tensor voting (TV) & non-maximum suppression (NMS), and fitting based centerline connection. Specifically, FMCR-GC is developed to segment the road region from the image by incorporating multiple features and multiscale fusion. In this way, homogenous road segmentation can be achieved. Then, TVNMS is introduced to generate a road centerline network. It not only extracts smooth road centerline, but also connects the discontinuous ones together. Finally, a fitting based algorithm is proposed to overcome the ineffectiveness of existing methods in the road intersections. Extensive experiments on two datasets demonstrate that our method achieves higher quantitative results, as well as more satisfactory visual performances by comparing with state-of-the-art methods.
KeywordRoad Centerline Extraction Multiscale Segmentation Tensor Voting Non-maximum Suppression Fitting Based Centerline Connection
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14534
Collection空天信息研究中心
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Cheng GL,Zhu FY,Xiang SM,et al. Accurate urban road centerline extraction from VHR imagery via multiscale segmentation and tensor voting[J]. Neurocomputing,2016(205):407-420.
APA Cheng GL,Zhu FY,Xiang SM,Wang Y,&Pan CH.(2016).Accurate urban road centerline extraction from VHR imagery via multiscale segmentation and tensor voting.Neurocomputing(205),407-420.
MLA Cheng GL,et al."Accurate urban road centerline extraction from VHR imagery via multiscale segmentation and tensor voting".Neurocomputing .205(2016):407-420.
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