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Multilevel Building Detection Framework in Remote Sensing Images Based on Convolutional Neural Networks
Liu, Yibo; Zhang, Zhenxin; Zhong, Ruofei; Chen, Dong1; Ke, Yinghai; Peethambaran, Jiju2; Chen, Chuqun3; Sun, Lan
2018
Source PublicationIEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
ISSN1939-1404
Volume11Issue:10Pages:3688
AbstractIn this paper, we propose a hierarchical building detection framework based on deep learning model, which focuses on accurately detecting buildings from remote sensing images. To this end, we first construct the generation model of the multilevel training samples using the Gaussian pyramid technique to learn the features of building objects at different scales and spatial resolutions. Then, the building region proposal networks are put forward to quickly extract candidate building regions, thereby increasing the efficiency of the building object detection. Based on the candidate building regions, we establish the multilevel building detection model using the convolutional neural networks (CNNs), from which the generic image features of each building region proposal are calculated. Finally, the obtained features are provided as inputs for training CNNs model, and the learned model is further applied to test images for the detection of unknown buildings. Various experiments using the Datasets I and II (in Section V-A) show that the proposed framework increases the mean average precision values of building detection by 3.63%, 3.85%, and 3.77%, compared with the state-of-the-art methods, i.e., Method IV. Besides, the proposed method is robust to the buildings having different spatial textures and types.
DepartmentLTO
KeywordBuilding detection convolutional neural networks (CNNs) candidate building regions multilevel framework remote sensing images
DOI10.1109/JSTARS.2018.2866284
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.scsio.ac.cn/handle/344004/17565
Collection热带海洋环境国家重点实验室(LTO)
Affiliation1.Capital Normal Univ, Adv Innovat Ctr Imaging Technol, Beijing 100048, Peoples R China
2.Nanjing Forestry Univ, Coll Civil Engn, Nanjing 210037, Jiangsu, Peoples R China
3.St Marys Univ, Dept Math & Comp, Halifax, NS B3H 3C3, Canada
4.Chinese Acad Sci, South China Sea Inst Oceanol, Guangdong Key Lab Ocean Remote Sensing, Guangzhou 510301, Guangdong, Peoples R China
Recommended Citation
GB/T 7714
Liu, Yibo,Zhang, Zhenxin,Zhong, Ruofei,et al. Multilevel Building Detection Framework in Remote Sensing Images Based on Convolutional Neural Networks[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2018,11(10):3688, 3700.
APA Liu, Yibo.,Zhang, Zhenxin.,Zhong, Ruofei.,Chen, Dong.,Ke, Yinghai.,...&Sun, Lan.(2018).Multilevel Building Detection Framework in Remote Sensing Images Based on Convolutional Neural Networks.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,11(10),3688.
MLA Liu, Yibo,et al."Multilevel Building Detection Framework in Remote Sensing Images Based on Convolutional Neural Networks".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 11.10(2018):3688.
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