Progressive Feature Fusion Framework Based on Graph Convolutional Network for Remote Sensing Scene Classification

Remote sensing (RS) scene classification plays an important role in the intelligent interpretation of RS data. Recently, convolutional neural network (CNN)-based and attention-based methods have become the mainstream of RS scene classification with impressive results. However, existing CNN-based methods do not utilize long-range information, and existing attention-based methods do not fully exploit multiscale information, although both aspects of information are essential for a comprehensive understanding of RS scene images. To overcome the above limitations, we propose a progressive feature fusion (PFF) framework based on graph convolutional network (GCN), namely PFFGCN for RS scene classification in this article, which has a strong ability to learn both multiscale and contextual (local/long-range) information in RS scene images. It mainly consists of two modules: a multilayer feature extraction module and a multiscale contextual information fusion (MCIF) module. The MFE module is utilized to extract multilevel features and global features, and the MCIF module is constructed to capture rich contextual information from multilevel features and fuse them in a progressive manner. In MCIF, GCN is adopted to explore intrinsic attributes (including the topological structure and the contextual information) hidden in each feature map. Through the PFF strategy, the graph features at each level are fused with the next-level features to reduce the semantic gap between nonadjacent features and enhance the multiscale representation of the model. Besides, grouped GCN based on channel grouping is further proposed to improve the efficiency of PFFGCN. The proposed method is extensively evaluated on various RS scene classification datasets, and the experimental results demonstrate that the proposed method outperforms current state-of-the-art methods.