Deep semantic understanding of high resolution remote sensing image | |
Qu, Bo1,2; Li, Xuelong1; Tao, Dacheng3; Lu, Xiaoqiang1 | |
2016-08-16 | |
会议名称 | 2016 International Conference on Computer, Information and Telecommunication Systems, CITS 2016 |
会议录名称 | IEEE CITS 2016 ; 2016 International Conference on Computer, Information and Telecommunication Systems |
会议日期 | 2016-07-06 |
会议地点 | Kunming, China |
出版者 | Institute of Electrical and Electronics Engineers Inc. |
产权排序 | 1 |
摘要 | With the rapid development of remote sensing technology, huge quantities of high resolution remote sensing images are available now. Understanding these images in semantic level is of great significance. Hence, a deep multimodal neural network model for semantic understanding of the high resolution remote sensing images is proposed in this paper, which uses both visual and textual information of the high resolution remote sensing images to generate natural sentences describing the given images. In the proposed model, the convolution neural network is utilized to extract the image feature, which is then combined with the text descriptions of the images by RNN or LSTMs. And in the experiments, two new remote sensing image;captions datasets are built at first. Then different kinds of CNNs with RNN or LSTMs are combined to find which is the best combination for caption generation. The experiments results prove that the proposed method achieves good performances in semantic understanding of high resolution remote sensing images. © 2016 IEEE. |
关键词 | Image Reconstruction Semantics |
作者部门 | 光学影像学习与分析中心 |
DOI | 10.1109/CITS.2016.7546397 |
收录类别 | EI ; ISTP |
ISBN号 | 9781509034406 |
语种 | 英语 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/28207 |
专题 | 光谱成像技术研究室 |
通讯作者 | Qu, Bo |
作者单位 | 1.Center for OPTical IMagery Analysis and Learning (OPTIMAL), Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, Shaanxi; 710119, China 2.University of the Chinese Academy of Sciences, 19A Yuquanlu, Beijing; 100049, China 3.Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology Sydney, 81 Broadway Street, Ultimo; NSW; 2007, Australia |
推荐引用方式 GB/T 7714 | Qu, Bo,Li, Xuelong,Tao, Dacheng,et al. Deep semantic understanding of high resolution remote sensing image[C]:Institute of Electrical and Electronics Engineers Inc.,2016. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Deep semantic unders(4266KB) | 会议论文 | 限制开放 | CC BY-NC-SA | 请求全文 |
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