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

文献类型：期刊论文

作者Cao, Yong1,2; Huo, Chunlei1,2; Xu, Nuo1,2; Zhang, Xin1,2; Xiang, Shiming1,2; Pan, Chunhong1,2
刊名IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
出版日期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
DOI10.1109/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
WOS记录号WOS:000757847800001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Program of China ; Guangxi Natural Science Foundation ; National Natural Science Foundation of China
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.

入库方式： OAI收割

来源：自动化研究所

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