迈向稳定的半监督遥感分割：协同引导与融合策略

Toward Stable Semi-Supervised Remote Sensing Segmentation via Co-Guidance and Co-Fusion

December 28, 2025

作者: Yi Zhou, Xuechao Zou, Shun Zhang, Kai Li, Shiying Wang, Jingming Chen, Congyan Lang, Tengfei Cao, Pin Tao, Yuanchun Shi

cs.AI

摘要

半监督遥感图像语义分割为缓解详尽标注负担提供了可行方案，但其本质上受困于伪标签漂移问题——这种因确认偏差导致训练过程中误差累积的现象。本文提出Co2S，一种融合视觉语言模型与自监督模型先验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，包含分别基于预训练CLIP和DINOv3初始化的两种ViT视觉基础模型，以抑制误差累积和伪标签漂移。为有效整合这些异质先验，引入显式-隐式语义协同引导机制：利用文本嵌入和可学习查询分别提供显式与隐式类别级引导，从而协同增强语义一致性。此外，开发了全局-局部特征协同融合策略，将CLIP捕获的全局上下文信息与DINOv3提取的局部细节有效融合，使模型能生成高精度分割结果。在六个主流数据集上的大量实验表明，该方法在不同划分协议和多样场景下均能保持领先性能，彰显其优越性。项目页面详见https://xavierjiezou.github.io/Co2S/。

English

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/.