用图像重建提升遥感图像少样本分类性能
Reconstruction Guided Few-shot Network For Remote Sensing Image Classification
- 通过遮蔽部分图像并重建，引导学习语义丰富的特征
- 在1-shot和5-shot下优于现有方法，在EuroSAT和PatternNet上均表现更优
- 方法简单通用，适合各类主干网络，适用于数据稀缺的遥感场景
少样本遥感图像分类因标注样本有限且地表类型差异大而极具挑战。本文提出重建引导的少样本网络（RGFS-Net），在增强对未见类别泛化能力的同时，保持已见类别的分类一致性。方法引入掩码图像重建任务，对输入图像部分区域进行遮蔽并重建，以促进语义丰富特征的学习。该辅助任务强化了空间理解，提升了低数据条件下的类别区分能力。我们在EuroSAT和PatternNet数据集上，采用1-shot和5-shot设置进行评估，结果表明该方法持续优于现有基线。所提方法简单有效，兼容标准主干网络，为少样本遥感分类提供稳健解决方案。代码已开源：https://github.com/stark0908/RGFS。
原文摘要 · Abstract (English)
Few-shot remote sensing image classification is challenging due to limited labeled samples and high variability in land-cover types. We propose a reconstruction-guided few-shot network (RGFS-Net) that enhances generalization to unseen classes while preserving consistency for seen categories. Our method incorporates a masked image reconstruction task, where parts of the input are occluded and reconstructed to encourage semantically rich feature learning. This auxiliary task strengthens spatial understanding and improves class discrimination under low-data settings. We evaluated the efficacy of EuroSAT and PatternNet datasets under 1-shot and 5-shot protocols, our approach consistently outperforms existing baselines. The proposed method is simple, effective, and compatible with standard backbones, offering a robust solution for few-shot remote sensing classification. Codes are available at https://github.com/stark0908/RGFS.
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