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

文献类型：期刊论文

作者Wang, Dongji1,2,3; Dong, Qiulei1,2,3
刊名IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
出版日期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
DOI10.1109/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
WOS记录号WOS:000732096400001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
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.

入库方式： OAI收割

来源：自动化研究所

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