MCFINet: Multidepth Convolution Network With Shallow-Deep Feature Integration for Semantic Labeling in Remote Sensing Images

doi:10.1109/LGRS.2021.3065039

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 机器人视觉团队

	MCFINet: Multidepth Convolution Network With Shallow-Deep Feature Integration for Semantic Labeling in Remote Sensing Images
	Wang, Dongji 1,2,3; Dong, Qiulei 1,2,3
刊名	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
	2021-03-19
页码	5
关键词	Labeling Convolution Semantics Feature extraction Remote sensing Kernel Fuses Convolutional neural networks multiscale contexts remote sensing images semantic labeling
ISSN号	1545-598X
DOI	10.1109/LGRS.2021.3065039
通讯作者	Dong, Qiulei(qldong@nlpr.ia.ac.cn)
英文摘要	Semantic labeling in remote sensing images is an important and challenging technique, which has attracted increasing attention recently in earth detection, environmental protection, land utilization, and so on. However, it remains a challenge on how to effectively label objects with varied scales and similar textures in literature. Addressing this challenge, we propose a multidepth convolution network with shallow-deep feature integration, called MCFINet, which could effectively integrate multiscale contexts and shallow-layer/deep-layer features for labeling various objects. In the proposed network, we design two new modules--a multidepth convolutional module (MDCM) and an adaptive feature integration module (AFIM). The MDCM employs multilayer convolutions with varied layer numbers but fixed small-sized kernels in parallel to capture multiscale contexts, while the AFIM adaptively integrates the shallow-layer and deep-layer features of the proposed network to capture more discriminant features for segmenting objects with similar textures. Extensive experimental results on two benchmark data sets demonstrate that MCFINet could achieve better performances than seven existing methods in most cases.
资助项目	National Natural Science Foundation of China[U1805264] ; National Natural Science Foundation of China[61991423] ; National Natural Science Foundation of China[61573359] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB32050100]
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000732096400001
资助机构	National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/46941]
专题	自动化研究所_模式识别国家重点实验室_机器人视觉团队
通讯作者	Dong, Qiulei
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wang, Dongji,Dong, Qiulei. MCFINet: Multidepth Convolution Network With Shallow-Deep Feature Integration for Semantic Labeling in Remote Sensing Images[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2021:5.
APA	Wang, Dongji,&Dong, Qiulei.(2021).MCFINet: Multidepth Convolution Network With Shallow-Deep Feature Integration for Semantic Labeling in Remote Sensing Images.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,5.
MLA	Wang, Dongji,et al."MCFINet: Multidepth Convolution Network With Shallow-Deep Feature Integration for Semantic Labeling in Remote Sensing Images".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2021):5.