TY - GEN
T1 - Hyperspectral Image Classification Based on Generative Adversarial Network with Dropblock
AU - Yin, Jihao
AU - Li, Wenyue
AU - Han, Bingnan
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - Deep learning (DL) algorithms are widely applied in hyperspectral images (HSIs) classification. However, the insufficient utilization in spatial semantic information and inadequate number of HSIs samples both restrict the classification performance of DL-based HSIs algorithms. In this paper, we propose a novel method based on generative adversarial network (GAN) with DropBlock structure (DBGAN). Specifically, DropBlock enforces each unit in convolution neural network (CNN) to learn features by dropping contiguous regions of feature maps, therefore more spatial semantic information is capable to contribute in HSIs classification. Furthermore, GAN model can generate realistic samples by an adversarial game to mitigate HSIs data shortage. Extensive experimental comparisons demonstrate the effectiveness of the proposed method.
AB - Deep learning (DL) algorithms are widely applied in hyperspectral images (HSIs) classification. However, the insufficient utilization in spatial semantic information and inadequate number of HSIs samples both restrict the classification performance of DL-based HSIs algorithms. In this paper, we propose a novel method based on generative adversarial network (GAN) with DropBlock structure (DBGAN). Specifically, DropBlock enforces each unit in convolution neural network (CNN) to learn features by dropping contiguous regions of feature maps, therefore more spatial semantic information is capable to contribute in HSIs classification. Furthermore, GAN model can generate realistic samples by an adversarial game to mitigate HSIs data shortage. Extensive experimental comparisons demonstrate the effectiveness of the proposed method.
KW - Hyperspectral classification
KW - generative adversarial networks
KW - spatial semantic information
UR - https://www.scopus.com/pages/publications/85076804605
U2 - 10.1109/ICIP.2019.8802936
DO - 10.1109/ICIP.2019.8802936
M3 - 会议稿件
AN - SCOPUS:85076804605
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 405
EP - 409
BT - 2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PB - IEEE Computer Society
T2 - 26th IEEE International Conference on Image Processing, ICIP 2019
Y2 - 22 September 2019 through 25 September 2019
ER -