Remote sensing scene classification based on high-order graph convolutional network

Remote sensing scene classification has gained increasing interest in remote sensing image understanding and feature representation is the crucial factor for classification methods. Convolutional Neural Network (CNN) generally uses hierarchical deep structure to automatically learn the feature representation from the whole images and thus has been widely applied in scene classification. However, it may fail to consider the discriminative components within the image during the learning process. Moreover, the potential relationships of scene semantics are likely to be ignored. In this paper, we present a novel remote sensing scene classification method based on high-order graph convolutional network (H-GCN). Our method uses the attention mechanism to focus on the key components inside the image during CNN feature learning. More importantly, high-order graph convolutional network is applied to investigate the class dependencies. The graph structure is built where each node is described by the mean of attentive CNN features from each semantic class. The semantic class dependencies are propagated with mixing neighbor information of nodes at different orders and thus the more informative representation of nodes can be gained. The node representations of H-GCN and attention CNN features are finally integrated as the discriminative feature representation for scene classification. Experimental results on benchmark datasets demonstrate the feasibility and effectiveness of our method for remote sensing scene classification.