A Feature Aggregation Convolutional Neural Network for Remote Sensing Scene Classification | |
Lu, Xiaoqiang1![]() ![]() | |
作者部门 | 光谱成像技术研究室 |
2019-10 | |
发表期刊 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
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ISSN | 0196-2892;1558-0644 |
卷号 | 57期号:10页码:7894-7906 |
产权排序 | 1 |
摘要 | Remote sensing scene classification (RSSC) refers to inferring semantic labels based on the content of the remote sensing scenes. Recently, most works take the pretrained convolutional neural network (CNN) as the feature extractor to build a scene representation for RSSC. The activations in different layers of CNN (named intermediate features) contain different spatial and semantic information. Recent works demonstrate that aggregating intermediate features into a scene representation can significantly improve the classification accuracy for RSSC. However, the intermediate features are aggregated by some unsupervised feature encoding methods (e.g., Bag-of-Visual-Words). Little attention has been paid to explore the information of semantic labels for the feature aggregation. In this paper, in order to explore the semantic label information, an end-to-end feature aggregation CNN (FACNN) is proposed to learn a scene representation for RSSC. In FACNN, a supervised convolutional features' encoding module and a progressive aggregation strategy are proposed to leverage the semantic label information to aggregate the intermediate features. The FACNN integrates the feature learning, feature aggregation, and classifier into a unified end-to-end framework for joint training. In FACNN, the scene representation is learned by considering the information of semantic labels, which can result in better performance for RSSC. Extensive experiments on AID, UC-Merged, and WHU-RS19 databases demonstrate that FACNN performs better than several state-of-the-art methods. |
关键词 | Feature aggregation remote sensing image scene classification |
DOI | 10.1109/TGRS.2019.2917161 |
收录类别 | SCI ; EI |
语种 | 英语 |
WOS记录号 | WOS:000489829200046 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
EI入藏号 | 20200408087082 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/31903 |
专题 | 光谱成像技术研究室 |
通讯作者 | Lu, Xiaoqiang |
作者单位 | 1.Chinese Acad Sci, Key Lab Spectral Imaging Technol CAS, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
推荐引用方式 GB/T 7714 | Lu, Xiaoqiang,Sun, Hao,Zheng, Xiangtao. A Feature Aggregation Convolutional Neural Network for Remote Sensing Scene Classification[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2019,57(10):7894-7906. |
APA | Lu, Xiaoqiang,Sun, Hao,&Zheng, Xiangtao.(2019).A Feature Aggregation Convolutional Neural Network for Remote Sensing Scene Classification.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,57(10),7894-7906. |
MLA | Lu, Xiaoqiang,et al."A Feature Aggregation Convolutional Neural Network for Remote Sensing Scene Classification".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 57.10(2019):7894-7906. |
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 | ||
A Feature Aggregatio(6634KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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