Mutual learning graph convolutional network for hyperspectral image classification

Abstract

Graph convolutional networks (GCNs) have achieved great success in hyperspectral image (HSI) classification. However, there are also some difficulties that need to be solved, such as the lack of enough labeled samples in the training process and the difference between the numbers of different land-cover analogies is too large, which will lead to poor classification performance. In order to alleviate those problems, we propose a Mutual Learning Graph Convolutional Network (called MLGCN) with an imbalance loss, which trains two GCN simultaneously, and these two GCNs can learn from each other by using mutual learning strategy in the training process. Experiments on three real data sets show that the proposed MLGCN method achieves state-of-the-art results in terms of three classification evaluation metrics, including overall accuracy (OA), average accuracy (AA), kappa coefficient (κ),.