| Title | Seed Feature Maps-based CNN Models for LEO Satellite Remote Sensing Services |
| Author | |
| DOI | |
| Publication Years | 2023
|
| ISSN | 2836-3876
|
| ISBN | 979-8-3503-0486-2
|
| Source Title | |
| Pages | 415-425
|
| Conference Date | 2-8 July 2023
|
| Conference Place | Chicago, IL, USA
|
| Keywords | |
| SUSTech Authorship | Others
|
| URL | [Source Record] |
| Data Source | IEEE
|
| PDF url | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10248321 |
| Citation statistics |
Cited Times [WOS]:0
|
| Document Type | Conference paper |
| Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/567781 |
| Department | Department of Computer Science and Engineering |
| Affiliation | 1.School of Software Engineering, Sun Yat-sen University, Zhuhai, China 2.School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China 3.Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China 4.Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China 5.New York University, New York, NY, USA 6.Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA |
| Recommended Citation GB/T 7714 |
Zhichao Lu,Chuntao Ding,Shangguang Wang,et al. Seed Feature Maps-based CNN Models for LEO Satellite Remote Sensing Services[C],2023:415-425.
|
| Files in This Item: | There are no files associated with this item. | |||||
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