S2TNet: Spectral–Spatial Triplet Network for Few-Shot Hyperspectral Image Classification-Reference-Cited by-同舟云学术

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S2TNet: Spectral–Spatial Triplet Network for Few-Shot Hyperspectral Image Classification

Published:2024 Issue: Volume:21 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Yue Guijie¹,Zhang Ling²,Zhou Yiyang³^ORCID,Wang Yuxiang⁴,Xue Zhaohui⁴^ORCID

Affiliation:

1. School of Architecture and Surveying Engineering, Beijing Polytechnic College, Beijing, China

2. School of Naval Architecture & Ocean Engineering, Jiangsu Maritime Institute, Nanjing, China

3. Artificial Intelligence Laboratory, Hangzhou Hikvision Digital Technology Company Ltd., Hangzhou, China

4. College of Geography and Remote Sensing, Hohai University, Nanjing, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

Natural Science Foundation of the Jiangsu Higher Education Institutions of China

Beijing Polytechnic College

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8859/10365397/10382615.pdf?arnumber=10382615

Reference14 articles.

1. Hyperspectral image data analysis

2. Artificial Intelligence for Remote Sensing Data Analysis: A review of challenges and opportunities

3. A survey: Deep learning for hyperspectral image classification with few labeled samples

4. Deep Few-Shot Learning for Hyperspectral Image Classification

5. Siamese Spectral Attention With Channel Consistency for Hyperspectral Image Classification

Cited by 1 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. Spectral-Spatial Mamba for Hyperspectral Image Classification;Remote Sensing;2024-07-03

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