Segmentation is an essential step for remote sensing image processing. This study aims to advance the application of the Segment Anything Model (SAM), an innovative image segmentation model by Meta AI, in the field of remote sensing image analysis. SAM is known for its exceptional generalization capabilities and zero-shot learning, making it a promising approach to processing aerial and orbital images from diverse geographical contexts. Our exploration involved testing SAM across multi-scale datasets using various input prompts, such as bounding boxes, individual points, and text descriptors. To enhance the model's performance, we implemented a novel automated technique that combines a text-prompt-derived general example with one-shot training. This adjustment resulted in an improvement in accuracy, underscoring SAM's potential for deployment in remote sensing imagery and reducing the need for manual annotation. Despite the limitations encountered with lower spatial resolution images, SAM exhibits promising adaptability to remote sensing data analysis. We recommend future research to enhance the model's proficiency through integration with supplementary fine-tuning techniques and other networks. Furthermore, we provide the open-source code of our modifications on online repositories, encouraging further and broader adaptations of SAM to the remote sensing domain.

翻译：分割是遥感图像处理的关键步骤。本研究旨在推动Meta AI创新图像分割模型Segment Anything Model（SAM）在遥感图像分析领域的应用。SAM以其卓越的泛化能力和零样本学习而著称，为处理来自不同地理环境的航空与轨道图像提供了极具前景的方法。我们通过多尺度数据集对SAM进行测试，使用了边界框、单点标注和文本描述等多种输入提示。为提升模型性能，我们提出了一种新型自动化技术，将文本提示生成的通用示例与单样本训练相结合。这一调整显著提高了分割精度，凸显了SAM在遥感影像部署中减少人工标注需求的潜力。尽管在低空间分辨率图像上存在局限，SAM仍展现出对遥感数据分析的良好适应性。我们建议未来研究通过集成辅助微调技术与其他网络来增强模型能力。此外，我们在开源代码仓库中提供了改进后的代码，以推动SAM在遥感领域的更广泛与深入应用。