针对遥感图像分类，提出自适应结构剪枝方法，显著降低计算量且保持高精度。
RemoteTrimmer: Adaptive Structural Pruning for Remote Sensing Image Classification
- 通过增强通道重要性差异来设计剪枝策略，更精准识别冗余参数。
- 在两个遥感数据集上压缩后准确率损失极小，达到当前最优水平。
- 适合需要轻量化部署的遥感图像分析场景，如边缘设备或实时处理。
高分辨率遥感图像分类通常需要较高的计算复杂度，轻量化模型更具实用性与效率。模型剪枝是有效的模型压缩方法。然而，现有方法很少考虑遥感图像的特异性，导致剪枝后准确率显著下降。为此，我们提出一种适用于遥感图像分类的有效结构剪枝方法。具体而言，引入一种放大模型通道重要性差异的剪枝策略；设计一种自适应挖掘损失函数，用于剪枝后模型的微调过程。最后，在两个遥感分类数据集上进行了实验。结果表明，该方法在压缩遥感分类模型后实现最小的准确率损失，达到当前最优（SoTA）性能。
原文摘要 · Abstract (English)
Since high resolution remote sensing image classification often requires a relatively high computation complexity, lightweight models tend to be practical and efficient. Model pruning is an effective method for model compression. However, existing methods rarely take into account the specificity of remote sensing images, resulting in significant accuracy loss after pruning. To this end, we propose an effective structural pruning approach for remote sensing image classification. Specifically, a pruning strategy that amplifies the differences in channel importance of the model is introduced. Then an adaptive mining loss function is designed for the fine-tuning process of the pruned model. Finally, we conducted experiments on two remote sensing classification datasets. The experimental results demonstrate that our method achieves minimal accuracy loss after compressing remote sensing classification models, achieving state-of-the-art (SoTA) performance.
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