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Published VersionCC BY V4.0, Open Access
Journal article
Hyper-CycleGAN: A New Adversarial Neural Network Architecture for Cross-Domain Hyperspectral Data Generation
Applied Sciences, Vol.15(8), pp.1-22
2025
Abstract
The scarcity of labeled training samples poses a significant challenge in hyperspectral image classification. Cross-scene classification has been shown to be an effective approach to tackle the problem of limited sample learning. This paper investigates the usage of generative adversarial networks (GANs) to enable collaborative artificial intelligence learning on hyperspectral datasets. We propose and design a specialized architecture, termed Hyper-CycleGAN, for heterogeneous transfer learning across source and target scenes. This architecture enables the establishment of bidirectional mappings through efficient adversarial training and merges both source-to-target and target-to-source generators. The proposed Hyper-CycleGAN architecture harnesses the strengths of GANs, along with custom modifications like the integration of multi-scale attention mechanisms to enhance feature learning capabilities specifically tailored for hyperspectral data. To address training instability, the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) loss discriminator is utilized. Additionally, a label smoothing technique is introduced to enhance the generalization capability of the generator, particularly in handling unlabeled samples, thus improving model robustness. Experimental results are performed to validate and confirm the effectiveness of the cross-domain Hyper-CycleGAN approach by demonstrating its applicability to two real-world cross-scene hyperspectral image datasets. Addressing the challenge of limited labeled samples in hyperspectral image classification, this research makes significant contributions and gives valuable insights for remote sensing, environmental monitoring, and medical imaging applications.
Details
- Title
- Hyper-CycleGAN: A New Adversarial Neural Network Architecture for Cross-Domain Hyperspectral Data Generation
- Authors
- Yibo He - Xi’an Jiaotong-Liverpool UniversityKah Phooi Seng - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringLi Minn Ang (Corresponding Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringBei Peng - University of LiverpoolXingyu Zhao - University of Warwick
- Publication details
- Applied Sciences, Vol.15(8), pp.1-22
- Publisher
- MDPI AG
- Date published
- 2025
- DOI
- 10.3390/app15084188
- ISSN
- 2076-3417
- Copyright note
- © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
- Data Availability
- Publicly available datasets were analyzed in this study. This data can be found here: https://www.ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes (accessed on 2 April 2025).
- Organisation Unit
- School of Science, Technology and Engineering; Engage Research Lab
- Language
- English
- Record Identifier
- 991127096302621
- Output Type
- Journal article
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- Domestic collaboration
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- Web Of Science research areas
- Chemistry, Multidisciplinary
- Engineering, Multidisciplinary
- Materials Science, Multidisciplinary
- Physics, Applied
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