Most logit-based knowledge distillation methods transfer soft labels from the teacher model to the student model via Kullback–Leibler divergence based on softmax, an exponential normalization function. However, this exponential nature of softmax tends to prioritize the largest class (target class) while neglecting smaller ones (non-target classes), leading to an oversight of the non-target classes’s significance. To address this issue, we propose Non-Target-Class-Enhanced Knowledge Distillation (NTCE-KD) to amplify the role of non-target classes both in terms of magnitude and diversity. Specifically, we present a magnitude-enhanced Kullback–Leibler (MKL) divergence multi-shrinking the target class to enhance the impact of non-target classes in terms of magnitude. Additionally, to enrich the diversity of non-target classes, we introduce a diversity-based data augmentation strategy (DDA), further enhancing overall performance. Extensive experimental results on the CIFAR-100 and ImageNet-1k datasets demonstrate that non-target classes are of great significance and that our method achieves state-of-the-art performance across a wide range of teacher–student pairs.

Remote sensing image (RSI) scene classification aims to identify semantic categories in RSI using neural networks. However, high-performance deep neural networks typically demand substantial storage and computational resources, making practical deployment challenging. Knowledge distillation has emerged as an effective technique for developing compact models that maintain high classification accuracy in RSI tasks. Existing knowledge distillation methods often overlook the high inter-class similarity in RSI scenes, leading to low-confidence soft labels from the teacher model, which can mislead the student model. Conversely, overly confident soft labels may discard valuable non-target information. Additionally, the significant intra-class variability in RSI contributes to instability in the model’s decision boundaries. To address these challenges, we propose an efficient method called instance-level scaling and dynamic margin-alignment knowledge distillation (ISDM) for RSI scene classification. To balance the target and non-target class influence, we apply an entropy regularization loss to scale the teacher model’s target class at the instance level. Moreover, we introduce dynamic margin alignment between the student and teacher models to improve the student’s discriminative capability. By optimizing soft labels and enhancing the student’s ability to distinguish between classes, our method reduces the effects of inter-class similarity and intra-class variability. Experimental results on three public RSI scene classification datasets (AID, UCMerced, and NWPU-RESISC) demonstrate that our method achieves state-of-the-art performance across all teacher–student pairs with lower computational costs. Additionally, we validate the generalization of our approach on general datasets, including CIFAR-100 and ImageNet-1k.