Hyperspectral Images Single-Source Domain Generalization Based on Nonlinear Sample Generation-Reference-Cited by-同舟云学术

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Hyperspectral Images Single-Source Domain Generalization Based on Nonlinear Sample Generation

Published:2024 Issue: Volume:62 Page:1-13
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Wang Biqi¹,Xu Yang¹^ORCID,Wu Zebin¹^ORCID,Zheng Shangdong²^ORCID,Wei Zhihui¹^ORCID,Chanussot Jocelyn³^ORCID

Affiliation:

1. School of Computer Science and Engineering, Nanjing University of Science and Technology (NJUST), Nanjing, China

2. School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China

3. Inria, CNRS, Grenoble INP, LJK, Université Grenoble Alpes, Grenoble, France

Funder

National Natural Science Foundation of China

Jiangsu Provincial Innovation Support Program

Jiangsu Provincial Natural Science Foundation of China

Jiangsu Provincial Key Research and Development Program

Open Foundations of Jiangsu Province Engineering Research Center of Airborne Detecting and Intelligent Perceptive Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10509843.pdf?arnumber=10509843

Reference47 articles.

1. Physics-Informed Hyperspectral Remote Sensing Image Synthesis With Deep Conditional Generative Adversarial Networks

2. Cross-Modality Contrastive Learning for Hyperspectral Image Classification

3. Hyperspectral Anomaly Detection: A Dual Theory of Hyperspectral Target Detection

4. DisOptNet: Distilling Semantic Knowledge From Optical Images for Weather-Independent Building Segmentation

5. ACGT-Net: Adaptive Cuckoo Refinement-Based Graph Transfer Network for Hyperspectral Image Classification

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