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.

翻译：分段任意模型（SAM）凭借其庞大的训练数据（SA-1B）展现出显著的通用性和零样本学习能力。鉴于SAM依赖于人工引导且具有类别不可知特性，我们发现其在遥感图像少样本语义分割任务中具有未被发掘的潜力。本研究提出了一种面向少样本语义分割自动化的结构化框架，该框架利用SAM模型实现更具语义可分辨性的分割结果高效生成。该方法的核心是一种新颖的自动提示学习方法，通过利用先验引导掩码为SAM生成粗粒度逐像素提示。在DLRSD数据集上的大量实验表明，该方法具有显著优越性，其表现优于现有的其他少样本方法。