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.

翻译：少样本遥感图像分类由于标记样本有限且地物类型变化性高而具有挑战性。我们提出了一种重建引导的少样本网络（RGFS-Net），该网络在增强对未见类别泛化能力的同时，保持了对已见类别的一致性。我们的方法引入了一项掩码图像重建任务，通过遮挡并重建输入图像的部分区域，以促进语义丰富的特征学习。该辅助任务增强了空间理解能力，并改善了低数据量场景下的类别区分度。我们在EuroSAT和PatternNet数据集上，采用1-shot和5-shot协议评估了方法的有效性，结果表明我们的方法始终优于现有基线。所提出的方法简单、有效，且与标准骨干网络兼容，为少样本遥感分类提供了一个稳健的解决方案。代码可在 https://github.com/stark0908/RGFS 获取。