Symmetrical Learning and Transferring: Efficient Knowledge Distillation for Remote Sensing Image Classification
Huaxiang Song, Junping Xie, Liang Liang, Yan Su, Yao Xiao, Xinyuan Zhang, Yuqi Ouyang, Xinling Li, Siyi Chen, Yucheng Li
Abstract
Knowledge distillation (KD) is crucial for remote sensing image (RSI) classification, particularly as the operating environment in remote sensing is often constrained by hardware limitations. However, prior research has not fully addressed the challenge of leveraging KD to develop lightweight, high-accuracy models for RSI classification. A key issue is the sparse distribution of training data, which often results in asymmetry within the data. This asymmetry impedes the transfer of prior knowledge during the distillation process, diminishing the overall efficacy of KD techniques. To overcome this challenge, we propose a novel, symmetry-enhanced approach that augments the logit-based KD process, improving its effectiveness and efficiency for RSI classification. Our method is distinguished by three core innovations: a symmetrically generative algorithm to enhance the symmetry of the training data, an efficient algorithm for constructing a robust teacher ensemble model, and a quantitative technique for feature alignment. Rigorous evaluations on three benchmark datasets demonstrate that our method outperforms 14 existing KD-based approaches and 30 other state-of-the-art methods. Specifically, the student model trained with our approach achieves accuracy improvements of up to 22.5% while reducing the model size and inference time by as much as 96% and 88%, respectively. In conclusion, this research makes a significant contribution to RSI classification by introducing an efficient and effective data symmetry-driven method to enhance the knowledge transferring efficiency of the logit-based KD process.