EFCNet: Ensemble Full Convolutional Network for Semantic Segmentation of High-Resolution Remote Sensing Images
文献类型：期刊论文
| 作者 | Chen, Li1; Dou, Xin1; Peng, Jian1; Li, Wenbo2 ; Sun, Bingyu2; Li, Haifeng1
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| 刊名 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
 |
| 出版日期 | 2021-05-04 |
| 关键词 | Convolution Feature extraction Kernel Training Remote sensing Image segmentation Spatial resolution Convolutional neural network (CNN) ensemble learning remote sensing semantic segmentation |
| ISSN号 | 1545-598X |
| DOI | 10.1109/LGRS.2021.3076093 |
| 通讯作者 | Li, Haifeng(lihaifeng@csu.edu.cn) |
| 英文摘要 | Convolutional neural networks (CNNs) have achieved remarkable results in semantic segmentation of high-resolution remote sensing images (HRRSIs). However, the scales and textures of HRRSIs are diverse, which makes it difficult for a fixed-layer CNN to obtain rich features. In this regard, we propose an end-to-end ensemble fully convolutional network (EFCNet), which mainly includes two modules: the adaptive fusion module (AFM) and the separable convolutional module (SCM). The AFM can fuse features of different scales based on ensemble learning, whereas the SCM can reduce the complexity of the model under multifeature fusion. In the experiment, we use UNet and PSPNet to verify the framework on the ISPRS Vaihingen and Potsdam datasets. The experimental results show that the EFCNet can effectively improve the final segmentation performance and reduce the complexity of the ensemble model. |
| 资助项目 | National Natural Science Foundation of China[41871302] ; National Natural Science Foundation of China[61773360] ; National Natural Science Foundation of China[41871364] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000732338300001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/126924]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Li, Haifeng |
| 作者单位 | 1.Cent South Univ, Sch Geosci & Info Phys, Changsha 410083, Peoples R China 2.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Li,Dou, Xin,Peng, Jian,et al. EFCNet: Ensemble Full Convolutional Network for Semantic Segmentation of High-Resolution Remote Sensing Images[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2021. |
| APA | Chen, Li,Dou, Xin,Peng, Jian,Li, Wenbo,Sun, Bingyu,&Li, Haifeng.(2021).EFCNet: Ensemble Full Convolutional Network for Semantic Segmentation of High-Resolution Remote Sensing Images.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS. |
| MLA | Chen, Li,et al."EFCNet: Ensemble Full Convolutional Network for Semantic Segmentation of High-Resolution Remote Sensing Images".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2021). |
入库方式： OAI收割
来源：合肥物质科学研究院
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