CASIA OpenIR
Automatic Building Rooftop Extraction From Aerial Images via Hierarchical RGB-D Priors
Xu, Shibiao1; Pan, Xingjia1; Li, Er1; Wu, Baoyuan2; Bu, Shuhui3; Dong, Weiming1; Xiang, Shiming1; Zhang, Xiaopeng1
Source PublicationIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN0196-2892
2018-12-01
Volume56Issue:12Pages:7369-7387
Corresponding AuthorLi, Er(er.li@ia.ac.cn) ; Zhang, Xiaopeng(xiaopeng.zhang@ia.ac.cn)
AbstractAccurate building rooftop extraction from high-resolution aerial images is of crucial importance in a wide range of applications. Owing to the varying appearance and large-scale range of scene objects, especially for building rooftops in different scales and heights, single-scale or individual prior-based extraction technique is insufficient in pursuing efficient, generic, and accurate extraction results. The trend toward integrating multiscale or several cue techniques appears to be the best way; thus, such integration is the focus of this paper. We first propose a novel salient rooftop detector integrating four correlative RGB-D priors (depth cue, uniqueness prior, shape prior, and transition surface prior) for improved rooftop extraction to address the preceding complex issues mentioned. Then, these correlative cues are computed from image layers created by our multilevel segmentation and further fused into the state-of-the-art high-order conditional random field (CRF) framework to locate the rooftop. Finally, an iterative optimization strategy is applied for high-quality solving, which can robustly handle varying appearance of building rooftops. Performance evaluations in the SZTAKI-INRIA benchmark data sets show that our method outperforms the traditional color-based algorithm and the original high-order CRF algorithm and its variants. The proposed algorithm is also evaluated and found to produce consistently satisfactory results for various large-scale, real-world data sets.
KeywordHigh-order conditional random field (CRF) multilevel segmentation RGB-D priors rooftop extraction
DOI10.1109/TGRS.2018.2850972
WOS KeywordSALIENCY DETECTION ; LIDAR DATA ; SEGMENTATION ; RECOGNITION ; SELECTION ; RECOVERY ; STEREO ; DENSE
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61620106003] ; National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[61671451] ; National Natural Science Foundation of China[61771026] ; National Natural Science Foundation of China[61502490]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000451621000041
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25686
Collection中国科学院自动化研究所
Corresponding AuthorLi, Er; Zhang, Xiaopeng
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Tencent AI Lab, Shenzhen 518000, Peoples R China
3.Northwestern Polytech Univ, Sch Aeronaut, Xian 710072, Shaanxi, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Xu, Shibiao,Pan, Xingjia,Li, Er,et al. Automatic Building Rooftop Extraction From Aerial Images via Hierarchical RGB-D Priors[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2018,56(12):7369-7387.
APA Xu, Shibiao.,Pan, Xingjia.,Li, Er.,Wu, Baoyuan.,Bu, Shuhui.,...&Zhang, Xiaopeng.(2018).Automatic Building Rooftop Extraction From Aerial Images via Hierarchical RGB-D Priors.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,56(12),7369-7387.
MLA Xu, Shibiao,et al."Automatic Building Rooftop Extraction From Aerial Images via Hierarchical RGB-D Priors".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 56.12(2018):7369-7387.
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