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Semantic labeling of high-resolution aerial images using an ensemble of fully convolutional networks
Sun, Xiaofeng1,2; Shen, Shuhan1,2; Lin, Xiangguo3; Hu, Zhanyi1,2
2017-12-05
发表期刊JOURNAL OF APPLIED REMOTE SENSING
卷号11期号:4页码:042617 1-18
文章类型Article
摘要High-resolution remote sensing data classification has been a challenging and promising research topic in the community of remote sensing. In recent years, with the rapid advances of deep learning, remarkable progress has been made in this field, which facilitates a transition from hand-crafted features designing to an automatic end-to-end learning. A deep fully convolutional networks (FCNs) based ensemble learning method is proposed to label the high-resolution aerial images. To fully tap the potentials of FCNs, both the Visual Geometry Group network and a deeper residual network, ResNet, are employed. Furthermore, to enlarge training samples with diversity and gain better generalization, in addition to the commonly used data augmentation methods (e.g., rotation, multiscale, and aspect ratio) in the literature, aerial images from other datasets are also collected for cross-scene learning. Finally, we combine these learned models to form an effective FCN ensemble and refine the results using a fully connected conditional random field graph model. Experiments on the ISPRS 2-D Semantic Labeling Contest dataset show that our proposed end-to-end classification method achieves an overall accuracy of 90.7%, a state-of-the-art in the field. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
关键词Semantic Labeling Fully Convolutional Network Aerial Images Convolutional Neural Network Ensemble Learning
WOS标题词Science & Technology ; Life Sciences & Biomedicine ; Technology
DOI10.1117/1.JRS.11.042617
关键词[WOS]CLASSIFICATION
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61333015 ; 41371405 ; 61632003 ; 61421004)
WOS研究方向Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号WOS:000417288700001
引用统计
被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/21737
专题模式识别国家重点实验室_机器人视觉
作者单位1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Chinese Acad Surveying & Mapping, Inst Photogrammetry & Remote Sensing, Beijing, Peoples R China
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Sun, Xiaofeng,Shen, Shuhan,Lin, Xiangguo,et al. Semantic labeling of high-resolution aerial images using an ensemble of fully convolutional networks[J]. JOURNAL OF APPLIED REMOTE SENSING,2017,11(4):042617 1-18.
APA Sun, Xiaofeng,Shen, Shuhan,Lin, Xiangguo,&Hu, Zhanyi.(2017).Semantic labeling of high-resolution aerial images using an ensemble of fully convolutional networks.JOURNAL OF APPLIED REMOTE SENSING,11(4),042617 1-18.
MLA Sun, Xiaofeng,et al."Semantic labeling of high-resolution aerial images using an ensemble of fully convolutional networks".JOURNAL OF APPLIED REMOTE SENSING 11.4(2017):042617 1-18.
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