Hyperspectral Image Classification With Contrastive Graph Convolutional Network

• URL: http://arxiv.org/abs/2205.11237v1
• Date: Wed, 11 May 2022 12:06:37 GMT
• Title: Hyperspectral Image Classification With Contrastive Graph Convolutional Network
• Authors: Wentao Yu, Sheng Wan, Guangyu Li, Jian Yang, Chen Gong
• Abstract summary: A Graph Convolutional Network (GCN) model with contrastive learning is proposed to explore the supervision signals contained in both spectral information and spatial relations. The experimental results on four typical benchmark datasets firmly demonstrate the effectiveness of the proposed ConGCN in both qualitative and quantitative aspects.
• Score: 38.43072371303967
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, Graph Convolutional Network (GCN) has been widely used in Hyperspectral Image (HSI) classification due to its satisfactory performance. However, the number of labeled pixels is very limited in HSI, and thus the available supervision information is usually insufficient, which will inevitably degrade the representation ability of most existing GCN-based methods. To enhance the feature representation ability, in this paper, a GCN model with contrastive learning is proposed to explore the supervision signals contained in both spectral information and spatial relations, which is termed Contrastive Graph Convolutional Network (ConGCN), for HSI classification. First, in order to mine sufficient supervision signals from spectral information, a semi-supervised contrastive loss function is utilized to maximize the agreement between different views of the same node or the nodes from the same land cover category. Second, to extract the precious yet implicit spatial relations in HSI, a graph generative loss function is leveraged to explore supplementary supervision signals contained in the graph topology. In addition, an adaptive graph augmentation technique is designed to flexibly incorporate the spectral-spatial priors of HSI, which helps facilitate the subsequent contrastive representation learning. The extensive experimental results on four typical benchmark datasets firmly demonstrate the effectiveness of the proposed ConGCN in both qualitative and quantitative aspects.

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