Mutual learning graph convolutional network for hyperspectral image classification

ChaoJie Jia; Kun Zhan

doi:10.1117/12.2640844

13 October 2022 Mutual learning graph convolutional network for hyperspectral image classification

ChaoJie Jia, Kun Zhan

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122871C (2022) https://doi.org/10.1117/12.2640844
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

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),.

Citation Download Citation

ChaoJie Jia and Kun Zhan "Mutual learning graph convolutional network for hyperspectral image classification", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122871C (13 October 2022); https://doi.org/10.1117/12.2640844

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