Few-Shot Learning With Attention-Weighted Graph Convolutional Networks For Hyperspectral Image Classification
Xinyi Tong, Jihao Yin, Bingnan Han, Hui Qv
Abstract
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.