CASIA OpenIR  > 模式识别国家重点实验室  > 机器人视觉
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
Source PublicationJOURNAL OF APPLIED REMOTE SENSING
2017-12-05
Volume11Issue:4Pages:042617 1-18
SubtypeArticle
AbstractHigh-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)
KeywordSemantic Labeling Fully Convolutional Network Aerial Images Convolutional Neural Network Ensemble Learning
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine ; Technology
DOI10.1117/1.JRS.11.042617
WOS KeywordCLASSIFICATION
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61333015 ; 41371405 ; 61632003 ; 61421004)
WOS Research AreaEnvironmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEnvironmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000417288700001
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21737
Collection模式识别国家重点实验室_机器人视觉
Affiliation1.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
Recommended Citation
GB/T 7714
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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