针对高分辨率遥感影像语义分割中像素级标注成本高昂、有限标注条件下地物边界模糊、细长结构易断裂及小目标易丢失的问题，提出一种融合视觉蒸馏与结构感知的半监督语义分割方法。基于FixMatch 半监督框架，构建多教师协同指导的视觉蒸馏架构，利用预训练的DINOv2与CLIP作为冻结教师模型，通过特征对齐将基础模型的通用视觉知识迁移至学生网络；设计细节保持与语义压缩模块，通过解耦高频细节与低频结构特征，增强对边界及小尺度地物的感知；提出双 路径参数化上下文融合模块与方向感知条带注意力模块，在解码阶段通过通道分组的可学习参数化机制保护细节信息，并强化线状地物的空间连续性。在LoveDA和DeepGlobe 公开数据集上的实验结果表明，仅使用10% 标注数据时，该方法在DeepGlobe 数据集上的平均交并比达到74.45%，平均F1 值达84.86%；在LoveDA数据集上的平均交并比达到55.71%，平均F1 值达69.43%，优于当前主流的半监督分割方法。消融实验验证了各模块对提升地物边界完整性与减少结构断裂的有效性。

Abstract： To address the challenges of high pixel-level annotation costs, ambiguous object boundaries under limited labeling, easy breakage of slender structures, and loss of small targets in high-resolution remote sensing image semantic segmentation, we propose a semi-supervised semantic segmentation method that integrates visual distillation with structural awareness. Based on the FixMatch semi-supervised framework, we design a multi-teacher collaborative visual distillation architecture, leveraging pre-trained DINOv2 and CLIP as frozen teacher models to transfer general visual knowledge from the base models to the student network through feature alignment. We introduce a detail-preserving and semantic compression module that decouples highfrequency details from low-frequency structural features, thereby enhancing boundary perception and detection of small-scale objects. Additionally, we propose a dual-path parameterized context fusion module and an orientationaware strip attention module, which employ learnable channel-grouped parameters during decoding to preserve fine details and strengthen spatial continuity for linear features. Experimental results on the public LoveDA and DeepGlobe datasets demonstrate that with only 10% labeled data, our method achieves an average intersection over union (IoU) of 74.45% and an average F1 score of 84.86% on the DeepGlobe dataset, and an average IoU of 55.71% and an average F1 score of 69.43% on the LoveDA dataset—outperforming current mainstream semisupervised segmentation methods. Ablation studies validate the effectiveness of each module in improving boundary completeness and reducing structural fragmentation.