Cross-domain few-shot learning based on a dual-prototype and state-space model for unmanned aerial vehicle hyperspectral image classification of coastal wetlands

XIN Ziqi; LI Zhongwei; YU Qixing; XU Mingming; REN Guangbo; WANG Leiquan; HU Yabin; ZHAO Dan

doi:10.11834/jrs.20264494

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Cross-domain few-shot learning based on a dual-prototype and state-space model for unmanned aerial vehicle hyperspectral image classification of coastal wetlands
- role：First author第一作者
- Affiliation：
  College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
- Email：B21160015@s.upc.edu.cn
- Introduction：辛紫麒，研究方向为跨场景高光谱图像分类。E-mail： B21160015@s.upc.edu.cn
XIN Ziqi
1 * ,
- role：Corresponding author通信作者
- Affiliation：
  College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
- Email：li.zhongwei@vip.163.com
- Introduction：李忠伟，研究方向为大数据智能处理及应用、光学及多手段融合与海岸带。E-mail： li.zhongwei@vip.163.com
LI Zhongwei
1 * ,
- Affiliation：
  College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
YU Qixing
1 * ,
- Affiliation：
  College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
  State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
XU Mingming
12 * ,
- Affiliation：
  First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
REN Guangbo
3 * ,
- Affiliation：
  Qingdao Institute of Software/College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
WANG Leiquan
4 * ,
- Affiliation：
  First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
HU Yabin
3 * ,
- Affiliation：
  State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
  College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
ZHAO Dan
25 *
Vol. 30, Issue 9, Pages: 2773-2792(2026)
DOI：10.11834/jrs.20264494
Accepted：

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Cross-domain few-shot learning based on a dual-prototype and state-space model for unmanned aerial vehicle hyperspectral image classification of coastal wetlands

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Cross-domain few-shot learning based on a dual-prototype and state-space model for unmanned aerial vehicle hyperspectral image classification of coastal wetlands

XIN Ziqi

LI Zhongwei

YU Qixing

XU Mingming

REN Guangbo

WANG Leiquan

HU Yabin

ZHAO Dan

DOI：10.11834/jrs.20264494

Full Text(HTML)

Figs( 39 ) Tabs( 7 )

References

Publication Info

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