Abstract
Few-shot semantic segmentation of time-series remote sensing images remains a critical challenge, particularly in regions where labeled data is scarce or costly to obtain. While state-of-the-art models perform well under full supervision, their performance degrades significantly under limited labeling, limiting their real-world applicability. In this work, we propose SAM-Aug, a new annotation-efficient framework that leverages the geometry-aware segmentation capability of the Segment Anything Model (SAM) to improve few-shot land cover mapping. Our approach constructs cloud-free composite images from temporal sequences and applies SAM in a fully unsupervised manner to generate geometry-aware mask priors. These priors are then integrated into training through a proposed loss function called RegionSmoothLoss, which enforces prediction consistency within each SAM-derived region across temporal frames, effectively regularizing the model to respect semantically coherent structures. Extensive experiments on the PASTIS-R benchmark under a 5 percent labeled setting demonstrate the effectiveness and robustness of SAM-Aug. Averaged over three random seeds (42, 2025, 4090), our method achieves a mean test mIoU of 36.21 percent, outperforming the state-of-the-art baseline by +2.33 percentage points, a relative improvement of 6.89 percent. Notably, on the most favorable split (seed=42), SAM-Aug reaches a test mIoU of 40.28 percent, representing an 11.2 percent relative gain with no additional labeled data. The consistent improvement across all seeds confirms the generalization power of leveraging foundation model priors under annotation scarcity. Our results highlight that vision models like SAM can serve as useful regularizers in few-shot remote sensing learning, offering a scalable and plug-and-play solution for land cover monitoring without requiring manual annotations or model fine-tuning.
Abstract (translated)
时间序列遥感图像的少样本语义分割仍然是一个关键挑战，尤其是在标记数据稀缺或获取成本高昂的地区。尽管最先进的模型在完全监督下表现良好，但它们在标注受限条件下的性能显著下降，限制了其实用性。在这项工作中，我们提出了SAM-Aug，这是一种新的注释高效框架，它利用Segment Anything Model（SAM）的几何感知分割能力来改进少样本土地覆盖制图。我们的方法通过从时间序列中构建无云合成图像，并以完全无监督的方式应用SAM生成几何感知掩模先验来实现这一点。然后通过引入名为RegionSmoothLoss的损失函数将这些先验集成到训练中，该函数强制每个SAM导出区域在时间帧之间保持预测一致性，从而有效规范模型尊重语义一致结构。 在PASTIS-R基准测试中的5%标注设置下进行了广泛的实验，证明了SAM-Aug的有效性和鲁棒性。经过三个随机种子（42, 2025, 4090）的平均测试mIoU为36.21%，比最先进的基线高出+2.33个百分点，相对改进率为6.89%。值得注意的是，在最有利分割（seed=42）中，SAM-Aug达到了40.28%的测试mIoU，代表了无额外标注数据的情况下11.2%的相对增益。所有种子上的持续改进证实了在标记稀缺条件下利用基础模型先验的一般化能力。 我们的结果表明，像SAM这样的视觉模型可以作为少样本遥感学习中的有用正则化器，在无需人工注释或模型微调的情况下提供可扩展且即插即用的土地覆盖监测解决方案。
URL
https://arxiv.org/abs/2601.09110
