A Remote Sensing Image Classification Method Based on Detail Attention Sampling and Teacher-Student Network

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With the continuous development of remote sensing technology, the data volume of high-resolution is increasing with the large coverage of high-resolution remote sensing images, changeable objects, and complex backgrounds. However, the sensitivity field of current convolutional neural networks is relatively small. This makes it difficult to capture information from the global context. Therefore, we propose remote sensing image classification with a Detailed Attention scheme and a Teacher-Student network named DATS to capture information in the global context. Firstly, the detailed attention scheme is used to integrate the spatial relationship of the feature graph into the feature channel. Thus feature graph is transformed into an attention map, generating structure-preserving and detail-preserving images. Then, the teacher-student network takes detail-preserving and structure-preserving images as inputs and uses feature refiners to enhance the fine-grained details of the images. Finally, fine-grained details learned from the teacher network are integrated into the main network by knowledge distillation, which achieves effective integration both in local detail features and global structure features. Experiments on FGSCR-42, WHU-RS19, and NWPU data sets showed that the Top-1 classification accuracy of our method reached 88.82%, 91.82%, and 87.60%, respectively.