Multilevel Building Detection Framework in Remote Sensing Images Based on Convolutional Neural Networks

doi:10.1109/JSTARS.2018.2866284

CORC > 南海海洋研究所 > 中国科学院南海海洋研究所 > 热带海洋环境国家重点实验室(LTO)

	Multilevel Building Detection Framework in Remote Sensing Images Based on Convolutional Neural Networks
	Peethambaran, Jiju 3; Sun, Lan ; Chen, Chuqun 1; Ke, Yinghai ; Chen, Dong 2; Zhong, Ruofei ; Zhang, Zhenxin ; Liu, Yibo
刊名	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
	2018
卷号	11 期号:10 页码:3688
关键词	Building detection convolutional neural networks (CNNs) candidate building regions multilevel framework remote sensing images
ISSN号	1939-1404
DOI	10.1109/JSTARS.2018.2866284
英文摘要	In 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.
内容类型	期刊论文
源URL	[http://ir.scsio.ac.cn/handle/344004/17565]
专题	南海海洋研究所_热带海洋环境国家重点实验室(LTO)
作者单位	1.St Marys Univ, Dept Math & Comp, Halifax, NS B3H 3C3, Canada 2.Capital Normal Univ, Adv Innovat Ctr Imaging Technol, Beijing 100048, Peoples R China 3.Nanjing Forestry Univ, Coll Civil Engn, Nanjing 210037, Jiangsu, Peoples R China 4.Chinese Acad Sci, South China Sea Inst Oceanol, Guangdong Key Lab Ocean Remote Sensing, Guangzhou 510301, Guangdong, Peoples R China
推荐引用方式 GB/T 7714	Peethambaran, Jiju,Sun, Lan,Chen, Chuqun,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	Peethambaran, Jiju.,Sun, Lan.,Chen, Chuqun.,Ke, Yinghai.,Chen, Dong.,...&Liu, Yibo.(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	Peethambaran, Jiju,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.