Feature Mixture Generative Adversarial Network for Data Augmentation on Small Sample Hyperspectral Data

Yulin Li, Mengmeng Zhang*, Xiaoming Xie*, Yunhao Gao, Wei Li

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

With the development of remote sensing technology, remote sensing data has been widely used in agriculture, medicine, military, and other fields. However, due to the disadvantages of the high cost of data collection and high redundancy, regression experiments using remote sensing data have serious overfitting problems. It limits its application in practical work. To alleviate this problem, we propose a generative adversarial network to generate remote sensing signals. In this paper, a feature mixing module was proposed to reduce the bias of the discriminator for different signals, thereby increasing the diversity of generated data. At the same time, spectral normalization is utilized to improve the stability during generation, which makes the generated data closer to the real signal. After a series of ablation experiments on small-sample remote sensing data, it is proved that the data generated by the generative adversarial network significantly improves the diversity of data and effectively alleviates the over-fitting problem based on ensuring the reliability of the data.

源语言英语
主期刊名ICIGP 2024 - Proceedings of the 2024 7th International Conference on Image and Graphics Processing
出版商Association for Computing Machinery
316-321
页数6
ISBN(电子版)9798400716720
DOI
出版状态已出版 - 19 1月 2024
活动7th International Conference on Image and Graphics Processing, ICIGP 2024 - Beijing, 中国
期限: 19 1月 202421 1月 2024

出版系列

姓名ACM International Conference Proceeding Series

会议

会议7th International Conference on Image and Graphics Processing, ICIGP 2024
国家/地区中国
Beijing
时期19/01/2421/01/24

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引用此

Li, Y., Zhang, M., Xie, X., Gao, Y., & Li, W. (2024). Feature Mixture Generative Adversarial Network for Data Augmentation on Small Sample Hyperspectral Data. 在 ICIGP 2024 - Proceedings of the 2024 7th International Conference on Image and Graphics Processing (页码 316-321). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3647649.3647699