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Automatic Building Rooftop Extraction From Aerial Images via Hierarchical RGB-D Priors

文献类型：期刊论文

作者Xu, Shibiao1; Pan, Xingjia1; Li, Er1; Wu, Baoyuan2; Bu, Shuhui3; Dong, Weiming1; Xiang, Shiming1; Zhang, Xiaopeng1
刊名IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
出版日期2018-12-01
卷号56期号:12页码:7369-7387
关键词High-order conditional random field (CRF) multilevel segmentation RGB-D priors rooftop extraction
ISSN号0196-2892
DOI10.1109/TGRS.2018.2850972
通讯作者Li, Er(er.li@ia.ac.cn) ; Zhang, Xiaopeng(xiaopeng.zhang@ia.ac.cn)
英文摘要Accurate 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.
WOS关键词SALIENCY DETECTION ; LIDAR DATA ; SEGMENTATION ; RECOGNITION ; SELECTION ; RECOVERY ; STEREO ; DENSE
资助项目National 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]
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000451621000041
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/25685]  
专题模式识别国家重点实验室_三维可视计算
通讯作者Li, Er; Zhang, Xiaopeng
作者单位1.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
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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