Scene classification for remote sensing images with self-attention augmented CNN

doi:10.1049/ipr2.12540

	Scene classification for remote sensing images with self-attention augmented CNN
	Liu, Zongyin 1; Dong, Anming 2,3; Yu, Jiguo 2,3; Han, Yubing 2,3; Zhou, You 4; Zhao, Kai5
发表期刊	IET IMAGE PROCESSING
ISSN	1751-9659
	2022-05-24
页码	12
通讯作者	Dong, Anming(anmingdong@qlu.edu.cn)
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.
DOI	10.1049/ipr2.12540
关键词[WOS]	CONVOLUTIONAL NEURAL-NETWORK ; BENCHMARK
收录类别	SCI
语种	英语
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]
项目资助者	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
WOS研究方向	Computer Science ; Engineering ; Imaging Science & Photographic Technology
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology
WOS记录号	WOS:000799668900001
出版者	WILEY
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/49519
专题	复杂系统认知与决策实验室_高效智能计算与学习
通讯作者	Dong, Anming
作者单位	1.Qilu Univ Technol, Sch Comp Sci & Technol, Shandong Acad Sci, Jinan, Peoples R China 2.Qilu Univ Technol, Big Data Inst, Shandong Acad Sci, Jinan, Peoples R China 3.Qilu Univ Technol, Sch Math & Stat, Shandong Acad Sci, Jinan, Peoples R China 4.Shandong HiCon New Media Inst Co Ltd, Technol Dept, Jinan, Peoples R China 5.Chinese Acad Sci, Inst Automat, Beijing, 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.