Remote Sensing Scene Classification by Gated Bidirectional Network | |
Sun, Hao1,2; Li, Siyuan1,2,3; Zheng, Xiangtao2; Lu, Xiaoqiang2 | |
刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING |
2020-01 | |
卷号 | 58期号:1页码:82-96 |
关键词 | Feature extraction Nonhomogeneous media Logic gates Aggregates Encoding Interference Task analysis Feature aggregation remote sensing (RS) image scene classification |
ISSN号 | 0196-2892;1558-0644 |
DOI | 10.1109/TGRS.2019.2931801 |
产权排序 | 1 |
英文摘要 | Remote sensing (RS) scene classification is a challenging task due to various land covers contained in RS scenes. Recent RS classification methods demonstrate that aggregating the multilayer convolutional features, which are extracted from different hierarchical layers of a convolutional neural network, can effectively improve classification accuracy. However, these methods treat the multilayer convolutional features as equally important and ignore the hierarchical structure of multilayer convolutional features. Multilayer convolutional features not only provide complementary information for classification but also bring some interference information (e.g., redundancy and mutual exclusion). In this paper, a gated bidirectional network is proposed to integrate the hierarchical feature aggregation and the interference information elimination into an end-to-end network. First, the performance of each convolutional feature is quantitatively analyzed and a superior combination of convolutional features is selected. Then, a bidirectional connection is proposed to hierarchically aggregate multilayer convolutional features. Both the top-down direction and the bottom-up direction are considered to aggregate multilayer convolutional features into the semantic-assist feature and appearance-assist feature, respectively, and a gated function is utilized to eliminate interference information in the bidirectional connection. Finally, the semantic-assist feature and appearance-assist feature are merged for classification. The proposed method can compete with the state-of-the-art methods on four RS scene classification data sets (AID, UC-Merced, WHU-RS19, and OPTIMAL-31). |
语种 | 英语 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:000507307800006 |
内容类型 | 期刊论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/93356] |
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 | Zheng, Xiangtao |
作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Key Lab Spectral Imaging Technol CAS, Xian 710119, Peoples R China 3.Xi An Jiao Tong Univ, Xian 710049, Peoples R China |
推荐引用方式 GB/T 7714 | Sun, Hao,Li, Siyuan,Zheng, Xiangtao,et al. Remote Sensing Scene Classification by Gated Bidirectional Network[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2020,58(1):82-96. |
APA | Sun, Hao,Li, Siyuan,Zheng, Xiangtao,&Lu, Xiaoqiang.(2020).Remote Sensing Scene Classification by Gated Bidirectional Network.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,58(1),82-96. |
MLA | Sun, Hao,et al."Remote Sensing Scene Classification by Gated Bidirectional Network".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 58.1(2020):82-96. |
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