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Semantic labeling of high-resolution aerial images using an ensemble of fully convolutional networks | |
Sun, Xiaofeng1,2![]() ![]() ![]() | |
发表期刊 | JOURNAL OF APPLIED REMOTE SENSING
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2017-12-05 | |
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 |
DOI | 10.1117/1.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 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/21737 |
专题 | 多模态人工智能系统全国重点实验室_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 |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 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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