Few-Shot Learning With Attention-Weighted Graph Convolutional Networks For Hyperspectral Image Classification

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.