TY - GEN
T1 - Few-Shot Learning with Attention-Weighted Graph Convolutional Networks for Hyperspectral Image Classification
AU - Tong, Xinyi
AU - Yin, Jihao
AU - Han, Bingnan
AU - Qv, Hui
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10
Y1 - 2020/10
N2 - In this paper, to alleviate the demand for enormous labeled data in the classification task, an Attention-weighted Graph Convolutional Networks (AwGCN) model for hyperspectral image (HSI) few-shot classification is proposed, which aims to explore the internal relationships of data for semi-supervised label propagation. To be specific, the attention-weighted graph is exploited to fully quantify the relationships of all samples, which is potential to solve the HSI few-shot learning problems. Subsequently, Graph Convolutional Networks (GCN) are applied to spread the labels, which ascertain the categories of samples based on the trained attention-weighted graph. The robust prediction of our proposed approach is validated on the real HSI and the experimental results show a competitive good performance, which demonstrates the superior ability of AwGCN in HSI few-shot classification.
AB - In this paper, to alleviate the demand for enormous labeled data in the classification task, an Attention-weighted Graph Convolutional Networks (AwGCN) model for hyperspectral image (HSI) few-shot classification is proposed, which aims to explore the internal relationships of data for semi-supervised label propagation. To be specific, the attention-weighted graph is exploited to fully quantify the relationships of all samples, which is potential to solve the HSI few-shot learning problems. Subsequently, Graph Convolutional Networks (GCN) are applied to spread the labels, which ascertain the categories of samples based on the trained attention-weighted graph. The robust prediction of our proposed approach is validated on the real HSI and the experimental results show a competitive good performance, which demonstrates the superior ability of AwGCN in HSI few-shot classification.
KW - Few-shot learning
KW - attention mechanism
KW - graph convolutional networks
KW - hyperspectral image classification
UR - https://www.scopus.com/pages/publications/85098628372
U2 - 10.1109/ICIP40778.2020.9190752
DO - 10.1109/ICIP40778.2020.9190752
M3 - 会议稿件
AN - SCOPUS:85098628372
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 1686
EP - 1690
BT - 2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings
PB - IEEE Computer Society
T2 - 2020 IEEE International Conference on Image Processing, ICIP 2020
Y2 - 25 September 2020 through 28 September 2020
ER -