TY - GEN
T1 - GAN-NL
T2 - 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018
AU - Duan, Yiping
AU - Tao, Xiaoming
AU - Xu, Mai
AU - Han, Chaoyi
AU - Lu, Jianhua
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Recently, deep learning methods have greatly enhanced the classification performance because of their strong representation ability in the local receptive field. However, the non-local spatial information always exist in the images. Moreover, the limited amount of the labeled data imposes great challenges on the supervised representation learning model, especially the remote sensing images. With the consideration, we propose a generative adversarial network with non-local spatial information (GAN-NL) for remote sensing image classification. Specifically, a non-local layer is incorporated into a generative adversarial network for unsupervised representation learning. Then, a classification network is designed to infer the labels of the images. The classification results on the challenging NWPU-RESISC45 remote sensing image dataset show that our proposed method performs favorably against the state-of-the-art methods in terms of the classification accuracy without any pre-training.
AB - Recently, deep learning methods have greatly enhanced the classification performance because of their strong representation ability in the local receptive field. However, the non-local spatial information always exist in the images. Moreover, the limited amount of the labeled data imposes great challenges on the supervised representation learning model, especially the remote sensing images. With the consideration, we propose a generative adversarial network with non-local spatial information (GAN-NL) for remote sensing image classification. Specifically, a non-local layer is incorporated into a generative adversarial network for unsupervised representation learning. Then, a classification network is designed to infer the labels of the images. The classification results on the challenging NWPU-RESISC45 remote sensing image dataset show that our proposed method performs favorably against the state-of-the-art methods in terms of the classification accuracy without any pre-training.
KW - Generative adversarial networks (GANs)
KW - Non-local spatial information
KW - Remote sensing image classification
KW - Unsupervised representation learning
UR - https://www.scopus.com/pages/publications/85063079226
U2 - 10.1109/GlobalSIP.2018.8646414
DO - 10.1109/GlobalSIP.2018.8646414
M3 - 会议稿件
AN - SCOPUS:85063079226
T3 - 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
SP - 375
EP - 379
BT - 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 November 2018 through 29 November 2018
ER -