Zero-Shot Remote Sensing Scene Classification Method Based on Local-Global Feature Fusion and Weight Mapping Loss-Reference-Cited by-同舟云学术

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Zero-Shot Remote Sensing Scene Classification Method Based on Local-Global Feature Fusion and Weight Mapping Loss

Published:2024 Issue: Volume:17 Page:2763-2776
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Wang Chao¹,Li Junyong¹^ORCID,Tanvir Ahmed¹^ORCID,Yang Jiajun¹^ORCID,Xie Tao²^ORCID,Ji Liqiang¹^ORCID,Zhang Tong¹^ORCID

Affiliation:

1. School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, China

2. School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing, China

Funder

Post-Doctoral Fund of Jiangsu Province

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

http://xplorestaging.ieee.org/ielx7/4609443/10330207/10368306.pdf?arnumber=10368306

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. MFINet: A Novel Zero-Shot Remote Sensing Scene Classification Network Based on Multimodal Feature Interaction;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

2. Review of Zero-Shot Remote Sensing Image Scene Classification;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

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