随着遥感卫星和无人机技术的快速发展，遥感图像的获取变得更加方便，实现图像内容的精准解析逐渐成为研究的热点。然而，现有遥感图像语义分割方法在仅有少量样本的情况下难以应对场景变化后出现的新类别目标，进而影响模型的整体性能。为应对遥感场景中可能出现的新类和新场景问题，本文引入了广义小样本分割技术，旨在利用少量样本快速适应新类，并高效完成遥感图像的语义分割任务。首先，针对遥感图像中不同类别和不同尺寸物体难以分辨的 挑战，引入多尺度特征融合解码器，提高了模型对不同尺度目标的分割能力。然后，针对广义小样本分割任务中的背景语义偏移问题，设计了背景一致性建模训练策略，保证了模型对背景特征的准确表达。此外，本文设计了类间关系挖掘技术，利用大量基类数据生成的原型，增强少量数据生成的新类原型，使其能够从基类中获取有利信息，从而实现更精确的分割。最后，在公开数据集上，本文与主流方法进行了对比实验，分割指标mIoU最高提升11.48%，证明了所提算法的有效性。

With the rapid development of remote sensing satellites and unmanned aerial vehicle technologies, the acquisition of remote sensing images has become more convenient, and the precise interpretation of image content has gradually become a research hotspot. However, existing remote sensing image semantic segmentation methods struggle to handle new object categories that emerge due to scene changes when only a small number of samples are available, thereby affecting the overall performance of the model. To address the potential issues of new classes and new scenes in remote sensing scenarios, this paper introduces the generalized few-shot segmentation technique, aiming to rapidly adapt to new classes using a few samples and efficiently complete the semantic segmentation task for remote sensing images. Firstly, to tackle the challenge of distinguishing objects of different categories and different sizes in remote sensing images, a multi-scale feature fusion decoder is introduced to enhance the model's segmentation ability for targets of different scales. Then, to address the background semantic shift problem in the generalized fewshot segmentation task, a background consistency modeling training strategy is designed to ensure the accurate expression of background features by the model. Furthermore, this paper proposes an inter-class relationship mining technique, which utilizes prototypes generated from a large amount of base class data to enhance the prototypes generated from a small amount of new class data, enabling them to acquire beneficial information from the base classes and achieve more precise segmentation. Finally, comparative experiments with mainstream methods are conducted on public datasets, and the mIoU segmentation metric is improved by up to 11.48%, demonstrating the effectiveness of the proposed algorithm.