Synthetic aircraft RS image modelling based on improved conditional GAN joint embedding network | |
Chen, Junyu1,2; Li, Haiwei1; Song, Liyao3; Zhang, Geng1; Hu, Bingliang1; Wang, Shuang1; Liu, Song1; Li, Siyuan1; Chen, Tieqiao1; Liu, Jia1 | |
作者部门 | 光谱成像技术研究室 |
2022-01-10 | |
发表期刊 | SCIENTIFIC REPORTS |
ISSN | 2045-2322 |
卷号 | 12期号:1 |
产权排序 | 1 |
摘要 | Developing an efficient and quality remote sensing (RS) technology using volume and efficient modelling in different aircraft RS images is challenging. Generative models serve as a natural and convenient simulation method. Because aircraft types belong to the fine class under the rough class, the issue of feature entanglement may occur while modelling multiple aircraft classes. Our solution to this issue was a novel first-generation realistic aircraft type simulation system (ATSS-1) based on the RS images. It realised fine modelling of the seven aircraft types based on a real scene by establishing an adaptive weighted conditional attention generative adversarial network and joint geospatial embedding (GE) network. An adaptive weighted conditional batch normalisation attention block solved the subclass entanglement by reassigning the intra-class-wise characteristic responses. Subsequently, an asymmetric residual self-attention module was developed by establishing a remote region asymmetric relationship for mining the finer potential spatial representation. The mapping relationship between the input RS scene and the potential space of the generated samples was explored through the GE network construction that used the selected prior distribution z, as an intermediate representation. A public RS dataset (OPT-Aircraft_V1.0) and two public datasets (MNIST and Fashion-MNIST) were used for simulation model testing. The results demonstrated the effectiveness of ATSS-1, promoting further development of realistic automatic RS simulation. |
DOI | 10.1038/s41598-021-03880-x |
收录类别 | SCI |
语种 | 英语 |
WOS记录号 | WOS:000741645800049 |
出版者 | HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/95691 |
专题 | 光谱成像技术研究室 |
通讯作者 | Li, Haiwei; Zhang, Geng; Hu, Bingliang |
作者单位 | 1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Key Lab Spectral Imaging Technol, Xian 710119, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Xi An Jiao Tong Univ, Sch Informat & Commun Engn, Xian 710049, Peoples R China |
推荐引用方式 GB/T 7714 | Chen, Junyu,Li, Haiwei,Song, Liyao,et al. Synthetic aircraft RS image modelling based on improved conditional GAN joint embedding network[J]. SCIENTIFIC REPORTS,2022,12(1). |
APA | Chen, Junyu.,Li, Haiwei.,Song, Liyao.,Zhang, Geng.,Hu, Bingliang.,...&Liu, Jia.(2022).Synthetic aircraft RS image modelling based on improved conditional GAN joint embedding network.SCIENTIFIC REPORTS,12(1). |
MLA | Chen, Junyu,et al."Synthetic aircraft RS image modelling based on improved conditional GAN joint embedding network".SCIENTIFIC REPORTS 12.1(2022). |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Synthetic aircraft R(3786KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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