LAUSR

Scene classification is an active research topic in the remote sensing community, and complex spatial layouts with various types of objects bring huge challenges to classification. Convolutional neural network (CNN)-based methods attempt to explore the global features by gradually expanding the receptive field, while long-range contextual information is ignored. Vision transformer (ViT) can extract contextual features, but the learning ability of local information is limited, and it has a large computational complexity simultaneously. In this article, an end-to-end method is exploited by employing ViT as an excellent teacher for guiding small networks (ET-GSNet) in the remote sensing image scene classification. In the ET-GSNet, ResNet18 is selected as the student model, which integrates the superiorities of the two models via knowledge distillation (KD), and the computational complexity does not increase. In the KD process, the ViT and ResNet18 are optimized together without independent pretraining, and the learning rate of teacher model gradually decreases until zero, while the weight coefficient of the KD loss module is doubled. Based on the above procedures, dark knowledge from the teacher model can be transferred to the student model more smoothly. Experimental results on the four public remote sensing datasets demonstrate that the proposed ET-GSNet method possesses the superior classification performance compared to some state-of-the-art (SOTA) methods. In addition, we evaluate the ET-GSNet on a fine-grained ship recognition dataset, and the results show that our method has good generalization for different tasks in terms of some metrics.