EFCNet: Ensemble Full Convolutional Network for Semantic Segmentation of High-Resolution Remote Sensing Images

doi:10.1109/LGRS.2021.3076093

HFCAS OpenIR

	EFCNet: Ensemble Full Convolutional Network for Semantic Segmentation of High-Resolution Remote Sensing Images
	Chen, Li 1; Dou, Xin 1; Peng, Jian 1; Li, Wenbo2 ; Sun, Bingyu2 ; Li, Haifeng 1
	2021-05-04
发表期刊	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
ISSN	1545-598X
通讯作者	Li, Haifeng(lihaifeng@csu.edu.cn)
摘要	Convolutional neural networks (CNNs) have achieved remarkable results in semantic segmentation of high-resolution remote sensing images (HRRSIs). However, the scales and textures of HRRSIs are diverse, which makes it difficult for a fixed-layer CNN to obtain rich features. In this regard, we propose an end-to-end ensemble fully convolutional network (EFCNet), which mainly includes two modules: the adaptive fusion module (AFM) and the separable convolutional module (SCM). The AFM can fuse features of different scales based on ensemble learning, whereas the SCM can reduce the complexity of the model under multifeature fusion. In the experiment, we use UNet and PSPNet to verify the framework on the ISPRS Vaihingen and Potsdam datasets. The experimental results show that the EFCNet can effectively improve the final segmentation performance and reduce the complexity of the ensemble model.
关键词	Convolution Feature extraction Kernel Training Remote sensing Image segmentation Spatial resolution Convolutional neural network (CNN) ensemble learning remote sensing semantic segmentation
DOI	10.1109/LGRS.2021.3076093
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[41871302] ; National Natural Science Foundation of China[61773360] ; National Natural Science Foundation of China[41871364]
项目资助者	National Natural Science Foundation of China
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000732338300001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.hfcas.ac.cn:8080/handle/334002/126925
专题	中国科学院合肥物质科学研究院
通讯作者	Li, Haifeng
作者单位	1.Cent South Univ, Sch Geosci & Info Phys, Changsha 410083, Peoples R China 2.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China
推荐引用方式 GB/T 7714	Chen, Li,Dou, Xin,Peng, Jian,et al. EFCNet: Ensemble Full Convolutional Network for Semantic Segmentation of High-Resolution Remote Sensing Images[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2021.
APA	Chen, Li,Dou, Xin,Peng, Jian,Li, Wenbo,Sun, Bingyu,&Li, Haifeng.(2021).EFCNet: Ensemble Full Convolutional Network for Semantic Segmentation of High-Resolution Remote Sensing Images.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS.
MLA	Chen, Li,et al."EFCNet: Ensemble Full Convolutional Network for Semantic Segmentation of High-Resolution Remote Sensing Images".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2021).