TY - GEN
T1 - Generative Adversarial Network with Folded Spectrum for Hyperspectral Image Classification
AU - Li, Wenyue
AU - Yin, Jihao
AU - Han, Bingnan
AU - Zhu, Hongmei
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Hyperspectral image (HSIs) with abundant spectral information but limited labeled dataset endows the rationality and necessity of semi-supervised spectral-based classification methods. Where, the utilizing approach of spectral information is significant to classification accuracy. In this paper, we propose a novel semi-supervised method based on generative adversarial network (GAN) with folded spectrum (FS-GAN). Specifically, the original spectral vector is folded to 2D square spectrum as input of GAN, which can generate spectral texture and provide larger receptive field over both adjacent and non-adjacent spectral bands for deep feature extraction. The generated fake folded spectrum, the labeled and unlabeled real folded spectrum are then fed to the discriminator for semi-supervised learning. A feature matching strategy is applied to prevent model collapse. Extensive experimental comparisons demonstrate the effectiveness of the proposed method.
AB - Hyperspectral image (HSIs) with abundant spectral information but limited labeled dataset endows the rationality and necessity of semi-supervised spectral-based classification methods. Where, the utilizing approach of spectral information is significant to classification accuracy. In this paper, we propose a novel semi-supervised method based on generative adversarial network (GAN) with folded spectrum (FS-GAN). Specifically, the original spectral vector is folded to 2D square spectrum as input of GAN, which can generate spectral texture and provide larger receptive field over both adjacent and non-adjacent spectral bands for deep feature extraction. The generated fake folded spectrum, the labeled and unlabeled real folded spectrum are then fed to the discriminator for semi-supervised learning. A feature matching strategy is applied to prevent model collapse. Extensive experimental comparisons demonstrate the effectiveness of the proposed method.
KW - Hyperspectral classification
KW - generative adversarial network
KW - semi-supervised learning
UR - https://www.scopus.com/pages/publications/85077697712
U2 - 10.1109/IGARSS.2019.8899034
DO - 10.1109/IGARSS.2019.8899034
M3 - 会议稿件
AN - SCOPUS:85077697712
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 883
EP - 886
BT - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Y2 - 28 July 2019 through 2 August 2019
ER -