Robust Space-Frequency Joint Representation for Remote Sensing Image Scene Classification | |
Fang, Jie1,2; Yuan, Yuan3; Lu, Xiaoqiang1; Feng, Yachuang1 | |
刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING |
2019-10 | |
卷号 | 57期号:10页码:7492-7502 |
关键词 | Frequency domain joint representation remote sensing image classification robust space domain |
ISSN号 | 0196-2892;1558-0644 |
DOI | 10.1109/TGRS.2019.2913816 |
产权排序 | 1 |
英文摘要 | Remote sensing image scene classification is a fundamental problem, which aims to label an image with a specific semantic category automatically. Recent progress on remote sensing image scene classification is substantial, benefitting mostly from the powerful feature extraction capability of convolutional neural networks (CNNs). Even though these CNN-based methods have achieved competitive performances, they only construct the representation of the image in location-sensitive space-domain. As a result, their representations are not robust to rotation-variant remote sensing images, which influence the classification accuracy. In this paper, we propose a novel feature representation method by introducing a frequency-domain branch to the traditional only-space-domain architecture. Our framework takes full advantages of discriminative features from space domain and location-robust features from the frequency domain, providing more advanced representations through an additional joint learning module, a property that is critically needed to perform remote sensing image scene classification. Additionally, our method produces satisfactory performances on four public and challenging remote sensing image scene data sets, Sydney, UC-Merced, WHU-RS19, and AID. |
语种 | 英语 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:000489829200016 |
内容类型 | 期刊论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/31902] |
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 |
作者单位 | 1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Key Lab Spectral Imaging Technol CAS, Xian 710119, Shaanxi, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Northwestern Polytech Univ, Ctr Opt Imagery Anal & Learning OPTIMAL, Xian 710072, Shaanxi, Peoples R China |
推荐引用方式 GB/T 7714 | Fang, Jie,Yuan, Yuan,Lu, Xiaoqiang,et al. Robust Space-Frequency Joint Representation for Remote Sensing Image Scene Classification[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2019,57(10):7492-7502. |
APA | Fang, Jie,Yuan, Yuan,Lu, Xiaoqiang,&Feng, Yachuang.(2019).Robust Space-Frequency Joint Representation for Remote Sensing Image Scene Classification.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,57(10),7492-7502. |
MLA | Fang, Jie,et al."Robust Space-Frequency Joint Representation for Remote Sensing Image Scene Classification".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 57.10(2019):7492-7502. |
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