Bidirectional adaptive feature fusion for remote sensing scene classification | |
Ji, Weijun1,2; Li, Xuelong1; Lu, Xiaoqiang1; Lu, Xiaoqiang (luxiaoqiang@opt.ac.cn) | |
2017 | |
会议日期 | 2017-10-11 |
会议地点 | Tianjin, China |
关键词 | Bidirectional adaptive feature fusion High spatial resolution remote Sensing images Scene classification |
卷号 | 772 |
DOI | 10.1007/978-981-10-7302-1_40 |
页码 | 486-497 |
英文摘要 | Convolutional neural networks (CNN) have been excellent for scene classification in nature scene. However, directly using the pre-trained deep models on the aerial image is not proper, because of the spatial scale variability and rotation variability of the HSR remote sensing images. In this paper, a bidirectional adaptive feature fusion strategy is investigated to deal with the remote sensing scene classification. The deep learning feature and the SIFT feature are fused together to get a discriminative image presentation. The fused feature can not only describe the scenes effectively by employing deep learning feature but also overcome the scale and rotation variability with the usage of the SIFT feature. By fusing both SIFT feature and global CNN feature, our method achieves state-of-the-art scene classification performance on the UCM and the AID datasets. © Springer Nature Singapore Pte Ltd. 2017. |
产权排序 | 1 |
会议录 | Computer Vision - 2nd CCF Chinese Conference, CCCV 2017, Proceedings |
会议录出版者 | Springer Verlag |
语种 | 英语 |
ISSN号 | 18650929 |
ISBN号 | 9789811073014 |
WOS记录号 | WOS:000449831600040 |
内容类型 | 会议论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/29613] |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
通讯作者 | Lu, Xiaoqiang (luxiaoqiang@opt.ac.cn) |
作者单位 | 1.Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an; Shaanxi; 710119, China 2.University of Chinese Academy of Sciences, 19A Yuquanlu, Beijing; 100049, China |
推荐引用方式 GB/T 7714 | Ji, Weijun,Li, Xuelong,Lu, Xiaoqiang,et al. Bidirectional adaptive feature fusion for remote sensing scene classification[C]. 见:. Tianjin, China. 2017-10-11. |
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