Remote Sensing Image Classification with Decoupled Knowledge Distillation
This provides an efficient solution for remote sensing applications on limited devices, though it appears incremental as it builds on existing knowledge distillation and lightweight network techniques.
The paper tackles the problem of deploying large remote sensing image classification models on resource-constrained devices by proposing a lightweight method using knowledge distillation, achieving nearly equivalent Top-1 accuracy while reducing parameters by 6.24 times compared to VGG-16.
To address the challenges posed by the large number of parameters in existing remote sensing image classification models, which hinder deployment on resource-constrained devices, this paper proposes a lightweight classification method based on knowledge distillation. Specifically, G-GhostNet is adopted as the backbone network, leveraging feature reuse to reduce redundant parameters and significantly improve inference efficiency. In addition, a decoupled knowledge distillation strategy is employed, which separates target and non-target classes to effectively enhance classification accuracy. Experimental results on the RSOD and AID datasets demonstrate that, compared with the high-parameter VGG-16 model, the proposed method achieves nearly equivalent Top-1 accuracy while reducing the number of parameters by 6.24 times. This approach strikes an excellent balance between model size and classification performance, offering an efficient solution for deployment on resource-limited devices.