The Segment Anything Model (SAM) exhibits remarkable versatility and
zero-shot learning abilities, owing largely to its extensive training data
(SA-1B). Recognizing SAM's dependency on manual guidance given its
category-agnostic nature, we identified unexplored potential within few-shot
semantic segmentation tasks for remote sensing imagery. This research
introduces a structured framework designed for the automation of few-shot
semantic segmentation. It utilizes the SAM model and facilitates a more
efficient generation of semantically discernible segmentation outcomes. Central
to our methodology is a novel automatic prompt learning approach, leveraging
prior guided masks to produce coarse pixel-wise prompts for SAM. Extensive
experiments on the DLRSD datasets underline the superiority of our approach,
outperforming other available few-shot methodologies.

本研究介绍了一种用于自动化少样本语义分割的结构化框架，利用 Segment Anything Model（SAM）模型，并借助先前引导的掩模生成粗略像素级提示，以获得更高效的语义可辨别分割结果，实验证明该方法在 DLRSD 数据集上优于其他可用的少样本方法。