A Deep Transfer Learning Framework Using Teacher–Student Structure for Land Cover Classification of Remote-Sensing Imagery

Deep-learning techniques are widely used for land cover classification in remote-sensing, primarily because they can effectively extract complex features from imagery data, which is essential for accurate land cover classification. However, the heterogeneity of data can pose a challenge to the generalizability of deep models. To address this issue, we propose a novel transfer learning framework for land cover classification using teacher–student structure. The proposed framework utilizes the knowledge acquired by the teacher model from large datasets to facilitate fine-tuning of the student model on small datasets, which prevents problems such as overfitting resulting from training large models on small datasets and inferior performance arising from the limited learning capacity of small models. To achieve this goal, we design a loss function based on central moment discrepancy (CMD) and high-temperature softmax. In addition, we conducted experiments on three distinct pairs of datasets and found that our proposed framework outperforms both training the student model from scratch on the target domain and simply fine-tuning the teacher and student models from the source domain to the target domain. Specifically, we observed an average increase in mean intersection over union (mIoU) of 9.9%, 2.1%, and 4.3%. These results demonstrate the effectiveness and generalizability of our proposed framework for remote-sensing land cover classification.