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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 | |
发表期刊 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING |
ISSN | 0196-2892 |
2018-12-01 | |
卷号 | 56期号:12页码:7369-7387 |
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. |
关键词 | High-order conditional random field (CRF) multilevel segmentation RGB-D priors rooftop extraction |
DOI | 10.1109/TGRS.2018.2850972 |
关键词[WOS] | SALIENCY DETECTION ; LIDAR DATA ; SEGMENTATION ; RECOGNITION ; SELECTION ; RECOVERY ; STEREO ; DENSE |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61771026] ; National Natural Science Foundation of China[61502490] ; National Natural Science Foundation of China[61671451] ; National Natural Science Foundation of China[61620106003] ; National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[61620106003] ; National Natural Science Foundation of China[61671451] ; National Natural Science Foundation of China[61502490] ; National Natural Science Foundation of China[61771026] |
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:000451621000041 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/25686 |
专题 | 多模态人工智能系统全国重点实验室_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 |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
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. |
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