FROM COARSE TO FINE: KNOWLEDGE DISTILLATION FOR REMOTE SENSING SCENE CLASSIFICATION

Scene classification is one of the most commonly studied areas of parsing the earth observation data. How to effectively interpreting the remote sensing images and extracting informative features are the great challenges for remote sensing image classification. Many important applications, such as land management and urban analysis, are based on the performance of remote sensing classification model. Recently, a lot of CNN based methods have been proposed and achieve promising results. Inspired by the success of knowledge distillation which transfers the learned information from a teacher model to a student model, a knowledge distillation based framework is proposed in this paper to handle the task of remote sensing scene classification from coarse to fine. Specifically, the learned knowledge from the teacher network is transformed into the coarse soft label and fine output mask to better guiding the student network to learn more informative features. Experiments are conducted on two widely used remote sensing scene datasets to evaluate the effectiveness of the proposed method and achieve comparable results compared with some state-of-the-art methods.