Remote Sensing Image Classification with Decoupled Knowledge Distillation

• URL: http://arxiv.org/abs/2505.19111v2
• Date: Mon, 09 Jun 2025 10:02:29 GMT
• Title: Remote Sensing Image Classification with Decoupled Knowledge Distillation
• Authors: Yaping He, Jianfeng Cai, Qicong Hu, Peiqing Wang,
• Abstract summary: This paper proposes a lightweight classification method based on knowledge distillation.<n>The proposed method achieves nearly equivalent Top-1 accuracy while reducing the number of parameters by 6.24 times.
• Score: 2.698114369639173
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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