Scene classification for remote sensing images with self-attention augmented CNN
文献类型：期刊论文
| 作者 | Liu, Zongyin5; Dong, Anming3,4; Yu, Jiguo3,4; Han, Yubing3,4; Zhou, You2; Zhao, Kai1
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| 刊名 | IET IMAGE PROCESSING
 |
| 出版日期 | 2022-05-24 |
| 页码 | 12 |
| ISSN号 | 1751-9659 |
| DOI | 10.1049/ipr2.12540 |
| 通讯作者 | Dong, Anming(anmingdong@qlu.edu.cn) |
| 英文摘要 | Remote sensing scene classification aims to automatically assign a specific semantic label to each image. It is challenging to classify remote sensing scene images due to the images' diversity and rich spatial information. Recently, convolutional neural networks have been widely used to overcome these difficulties, such as the famous Visual Geometry Group (VGG) network. However, the VGG network with local receptive fields cannot model the global information of remote sensing images well. It also needs a large number of parameters and floating point operations to achieve satisfactory accuracy. To overcome these challenges, we introduce the self-attention mechanism to the VGG network. Specifically, we replace the last four convolutional layers in the VGG-19 network with two cascaded self-attention blocks, each consisting of two multi-head self-attention (MHSA) layers with the residual network structure. The new structure can simultaneously explore the local and global information from remote sensing scenes. Such improvements not only reduce model parameters but also improve the classification performance. The effectiveness of the proposed method is validated through experiments on four public data sets, i.e., NaSC-TG2, WHU-RS19, AID and EuroSAT. |
| WOS关键词 | CONVOLUTIONAL NEURAL-NETWORK ; BENCHMARK |
| 资助项目 | National Key R&D Program of China[2019YFB2102600] ; National Natural Science Foundation of China[61701269] ; National Natural Science Foundation of China[61832012] ; National Natural Science Foundation of China[61771289] ; Opening Project of Shanghai Trusted Industrial Control Platform[TICPSH202103018-ZC] ; Fundamental Research Enhancement Program of Computer Science and Technology in Qilu University of Technology (Shandong Academy of Sciences)[2021JC02014] ; Joint Research Fund for Young Scholars in Qilu University of Technology (Shandong Academy of Sciences)[2017BSHZ005] ; Program for Youth Innovative Research Team in University of Shandong Province[2019KJN010] |
| WOS研究方向 | Computer Science ; Engineering ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000799668900001 |
| 出版者 | WILEY |
| 资助机构 | National Key R&D Program of China ; National Natural Science Foundation of China ; Opening Project of Shanghai Trusted Industrial Control Platform ; Fundamental Research Enhancement Program of Computer Science and Technology in Qilu University of Technology (Shandong Academy of Sciences) ; Joint Research Fund for Young Scholars in Qilu University of Technology (Shandong Academy of Sciences) ; Program for Youth Innovative Research Team in University of Shandong Province |
| 源URL | [http://ir.ia.ac.cn/handle/173211/49518]  |
| 专题 | 类脑芯片与系统研究 |
| 通讯作者 | Dong, Anming |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 2.Shandong HiCon New Media Inst Co Ltd, Technol Dept, Jinan, Peoples R China 3.Qilu Univ Technol, Sch Math & Stat, Shandong Acad Sci, Jinan, Peoples R China 4.Qilu Univ Technol, Big Data Inst, Shandong Acad Sci, Jinan, Peoples R China 5.Qilu Univ Technol, Sch Comp Sci & Technol, Shandong Acad Sci, Jinan, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Zongyin,Dong, Anming,Yu, Jiguo,et al. Scene classification for remote sensing images with self-attention augmented CNN[J]. IET IMAGE PROCESSING,2022:12. |
| APA | Liu, Zongyin,Dong, Anming,Yu, Jiguo,Han, Yubing,Zhou, You,&Zhao, Kai.(2022).Scene classification for remote sensing images with self-attention augmented CNN.IET IMAGE PROCESSING,12. |
| MLA | Liu, Zongyin,et al."Scene classification for remote sensing images with self-attention augmented CNN".IET IMAGE PROCESSING (2022):12. |
入库方式： OAI收割
来源：自动化研究所
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