Semi-supervised remote sensing (RS) image semantic segmentation offers a promising solution to alleviate the burden of exhaustive annotation, yet it fundamentally struggles with pseudo-label drift, a phenomenon where confirmation bias leads to the accumulation of errors during training. In this work, we propose Co2S, a stable semi-supervised RS segmentation framework that synergistically fuses priors from vision-language models and self-supervised models. Specifically, we construct a heterogeneous dual-student architecture comprising two distinct ViT-based vision foundation models initialized with pretrained CLIP and DINOv3 to mitigate error accumulation and pseudo-label drift. To effectively incorporate these distinct priors, an explicit-implicit semantic co-guidance mechanism is introduced that utilizes text embeddings and learnable queries to provide explicit and implicit class-level guidance, respectively, thereby jointly enhancing semantic consistency. Furthermore, a global-local feature collaborative fusion strategy is developed to effectively fuse the global contextual information captured by CLIP with the local details produced by DINOv3, enabling the model to generate highly precise segmentation results. Extensive experiments on six popular datasets demonstrate the superiority of the proposed method, which consistently achieves leading performance across various partition protocols and diverse scenarios. Project page is available at https://xavierjiezou.github.io/Co2S/.

翻译：半监督遥感图像语义分割为缓解详尽标注负担提供了一种有前景的解决方案，但其本质上受伪标签漂移现象的困扰——即训练过程中确认偏差导致误差累积的问题。本研究提出Co2S，一个通过协同融合视觉-语言模型与自监督模型先验知识的稳定半监督遥感分割框架。具体而言，我们构建了异构双学生架构，包含两个分别以预训练CLIP和DINOv3初始化的差异化ViT视觉基础模型，以缓解误差累积和伪标签漂移。为有效整合这些差异化先验，我们引入了显式-隐式语义协同引导机制，分别利用文本嵌入和可学习查询提供显式与隐式的类别级引导，从而共同增强语义一致性。此外，开发了全局-局部特征协同融合策略，有效融合CLIP捕获的全局上下文信息与DINOv3生成的局部细节，使模型能够生成高精度分割结果。在六个主流数据集上的大量实验证明了所提方法的优越性，其在多种数据划分协议和多样化场景中均取得领先性能。项目页面详见https://xavierjiezou.github.io/Co2S/。