HENet: Head-Level Ensemble Network for Very High Resolution Remote Sensing Images Semantic Segmentation

doi:10.1109/LGRS.2022.3147857

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 > 遥感图像处理团队

	HENet: Head-Level Ensemble Network for Very High Resolution Remote Sensing Images Semantic Segmentation
	Cao, Yong 1,2; Huo, Chunlei 1,2; Xu, Nuo 1,2; Zhang, Xin 1,2; Xiang, Shiming 1,2; Pan, Chunhong 1,2
刊名	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
	2022
卷号	19 页码:5
关键词	Head Computational modeling Semantics Image segmentation Feature extraction Correlation Mathematical models Cooperative learning (CL) ensemble learning semantic segmentation
ISSN号	1545-598X
DOI	10.1109/LGRS.2022.3147857
通讯作者	Huo, Chunlei(clhuo@nlpria.ac.cn)
英文摘要	Semantic segmentation plays an important role in very high resolution (VHR) image understanding. Despite the potentials of the deep convolutional network in improving performance by end-to-end feature learning, each model has its limitations, and it is hard to discriminate complex features purely by a single model. Ensemble learning is promising for integrating the strengths of different models, however, the ensemble of deep models is challenging due to the huge amount of parameters and computation of the deep model itself as well as the difficulty in capturing complementarity between different models. To tackle these problems, a head-level ensemble network (HENet) is proposed in this letter, which reduces model complexity by sharing feature extraction networks and improves complementarity between models by novel cooperative learning (CL). Experiments on ISPRS 2-D semantic labeling benchmark demonstrate the effectiveness and advantage of the proposed method.
资助项目	National Key Research and Development Program of China[2018AAA0100400] ; Guangxi Natural Science Foundation[2018GXNSFBA281086] ; National Natural Science Foundation of China[62071466] ; National Natural Science Foundation of China[61802407]
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:000757847800001
资助机构	National Key Research and Development Program of China ; Guangxi Natural Science Foundation ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/47909]
专题	自动化研究所_模式识别国家重点实验室_遥感图像处理团队
通讯作者	Huo, Chunlei
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Cao, Yong,Huo, Chunlei,Xu, Nuo,et al. HENet: Head-Level Ensemble Network for Very High Resolution Remote Sensing Images Semantic Segmentation[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2022,19:5.
APA	Cao, Yong,Huo, Chunlei,Xu, Nuo,Zhang, Xin,Xiang, Shiming,&Pan, Chunhong.(2022).HENet: Head-Level Ensemble Network for Very High Resolution Remote Sensing Images Semantic Segmentation.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,19,5.
MLA	Cao, Yong,et al."HENet: Head-Level Ensemble Network for Very High Resolution Remote Sensing Images Semantic Segmentation".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 19(2022):5.