A CNN-GCN Framework for Multi-Label Aerial Image Scene Classification

As one of the fundamental tasks in aerial image understanding, multi-label aerial image scene classification attracts increasing research interest. In general, the semantic category of a scene is reflected by the object information and the topological relations among objects. Most of existing deep learning-based aerial image scene classification methods (e.g., convolutional neural network (CNN)) classify the image scene by perceiving object information, while how to learn spatial relationships from image scene is still a challenging problem. In literature, graph convolutional network (GCN) has been successfully used for learning spatial characteristics of topological data, but it is rarely adopted in aerial image scene classification. To simultaneously mine both the object visual information and spatial relationships among multiple objects, this paper proposes a novel framework combining CNN and GCN to address multi-label aerial image scene classification. Extensive experimental results on two public datasets show that our proposed method can achieve better performance than the state-of-the-art methods.