TY - GEN
T1 - Feature Mixture Generative Adversarial Network for Data Augmentation on Small Sample Hyperspectral Data
AU - Li, Yulin
AU - Zhang, Mengmeng
AU - Xie, Xiaoming
AU - Gao, Yunhao
AU - Li, Wei
N1 - Publisher Copyright:
© 2024 ACM.
PY - 2024/1/19
Y1 - 2024/1/19
N2 - 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.
AB - 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.
KW - Data augmentation
KW - Deep learning
KW - Generative adversarial networks
UR - http://www.scopus.com/inward/record.url?scp=85192753512&partnerID=8YFLogxK
U2 - 10.1145/3647649.3647699
DO - 10.1145/3647649.3647699
M3 - Conference contribution
AN - SCOPUS:85192753512
T3 - ACM International Conference Proceeding Series
SP - 316
EP - 321
BT - ICIGP 2024 - Proceedings of the 2024 7th International Conference on Image and Graphics Processing
PB - Association for Computing Machinery
T2 - 7th International Conference on Image and Graphics Processing, ICIGP 2024
Y2 - 19 January 2024 through 21 January 2024
ER -