GLGAT-CFSL: Global-Local Graph Attention Network-Based Cross-Domain Few-Shot Learning for Hyperspectral Image Classification-Reference-Cited by-同舟云学术

GLGAT-CFSL: Global-Local Graph Attention Network-Based Cross-Domain Few-Shot Learning for Hyperspectral Image Classification

Published:2024 Issue: Volume:62 Page:1-19
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Ding Chen¹^ORCID,Deng Zhicong¹^ORCID,Xu Yaoyang¹^ORCID,Zheng Mengmeng¹^ORCID,Zhang Lei²^ORCID,Cao Yu³^ORCID,Wei Wei²^ORCID,Zhang Yanning²^ORCID

Affiliation:

1. School of Computer Science and Technology, Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, and Xi’an Key Laboratory of Big Data and Intelligent Computing, Xi’an University of Posts and Telecommunications, Xi’an, China

2. Shaanxi Provincial Key Laboratory of Speech and Image Information Processing and the National Engineering Laboratory for Integrated Aerospace-Ground-Ocean Big Data Application Technology, School of Computer Science, Northwestern Polytechnical University, Xi’an, China

3. Chinese Academy of Sciences, Xi’an Institute of Optics and Precision Mechanics and the Key Laboratory of Space Precision Measurement Technology, Xi’an, China

Funder

National Natural Science Foundation of China

National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/36/10354519/10557689.pdf?arnumber=10557689

Reference59 articles.

1. Self-Attention Guidance and Multiscale Feature Fusion-Based UAV Image Object Detection

2. An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing

3. Integrating Prototype Learning With Graph Convolution Network for Effective Active Hyperspectral Image Classification

4. Hyperspectral Image Classification Promotion Using Dynamic Convolution Based on Structural Re-Parameterization

5. Hyperspectral Image Classification: Potentials, Challenges, and Future Directions