Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
| HENet: Head-Level Ensemble Network for Very High Resolution Remote Sensing Images Semantic Segmentation | |
Cao, Yong1,2 ; Huo, Chunlei1,2; Xu, Nuo1,2; Zhang, Xin1,2; Xiang, Shiming1,2; Pan, Chunhong1,2
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| Source Publication | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
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| ISSN | 1545-598X |
| 2022 | |
| Volume | 19Pages:5 |
| Corresponding Author | Huo, Chunlei(clhuo@nlpria.ac.cn) |
| Abstract | 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. |
| Keyword | Head Computational modeling Semantics Image segmentation Feature extraction Correlation Mathematical models Cooperative learning (CL) ensemble learning semantic segmentation |
| DOI | 10.1109/LGRS.2022.3147857 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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] |
| Funding Organization | National Key Research and Development Program of China ; Guangxi Natural Science Foundation ; National Natural Science Foundation of China |
| WOS Research Area | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS Subject | Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS ID | WOS:000757847800001 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Sub direction classification | 图像视频处理与分析 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/47910 |
| Collection | 模式识别国家重点实验室_先进时空数据分析与学习 |
| Corresponding Author | Huo, Chunlei |
| Affiliation | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China |
| First Author Affilication | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Corresponding Author Affilication | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Recommended Citation 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. |
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