Toward Universal Representation Learning for Multidomain Hyperspectral Image Classification-Reference-Cited by-同舟云学术

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Toward Universal Representation Learning for Multidomain Hyperspectral Image Classification

Published:2023 Issue: Volume:61 Page:1-16
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Wang Jing¹^ORCID,Zhou Jun²^ORCID,Liu Xinwen³^ORCID

Affiliation:

1. Commonwealth Scientific and Industrial Research Organisation, Canberra, ACT, Australia

2. School of Information and Communication Technology, Griffith University, Nathan, QLD, Australia

3. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10092774.pdf?arnumber=10092774

Reference63 articles.

1. Efficient Parametrization of Multi-domain Deep Neural Networks

2. Universal representations: The missing link between faces, text, planktons, and cat breeds;bilen;arXiv 1701 07275,2017

3. Selecting Relevant Features from a Multi-domain Representation for Few-Shot Classification

Cited by 4 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. Online Seizure Prediction via Fine-Tuning and Test-Time Adaptation;IEEE Internet of Things Journal;2024-06-01

2. Masked Self-Distillation Domain Adaptation for Hyperspectral Image Classification;IEEE Transactions on Geoscience and Remote Sensing;2024

3. Cross-Domain Few-Shot Learning Based on Feature Disentanglement for Hyperspectral Image Classification;IEEE Transactions on Geoscience and Remote Sensing;2024

4. A Dual-Branch Deep Stochastic Adaptive Fourier Decomposition Network for Hyperspectral Image Classification;IEEE Transactions on Geoscience and Remote Sensing;2023

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