Frequency and spatial based multi-layer context network (FSCNet) for remote sensing scene classification

Remote Sensing Scene Classification (RSSC) is an important and challenging research topic due to the variety of land cover sizes and spatial combinations, as well as significant interclass similarity and intraclass variability. Currently, convolutional neural network (CNN)-based methods have been widely used in RSSC tasks with significant results. However, CNNs lack the ability to obtain long-term correlations. Transformer addressed this problem, thanks to the global receptive field of multi-head self-attention (MSA). Nevertheless, the vanilla transformer also needs further improvement to accommodate the diverse in type and scale of objects in RS scenes. In addition, the existing RSSC methods either use the last layer features, which is not conducive to process multi-scale remote sensing images, or directly fuse the multi-layer features, which will bring redundant or mutually exclusive information. To address the above issues, a novel RSSC framework, named frequency and spatial based multi-layer attention network (FSCNet) for remote sensing scene classification is proposed in this article. First, to fully extract the pyramid multi resolution features of CNN, a cross resolution injection model (CRIM) is proposed. Second, to generate better understand of the multilevel features, a frequency and spatial MLP (FS-MLP) is designed. Third, in order to aggregate contextual relations among multi-layer features, a multi-layer context align attention (MCAA) is adopted. The final classification is integration of top-layer feature and aggregated multi-layer feature. The experiment results on three well-known RS scene classification datasets (UCM, AID, and NWPU) prove the effectiveness of FSCNet and it outperforms many state-of-the-art methods.