Zero-OVCD：连接无训练基础模型与伪标签学习的开放词汇变化检测方法
Zero-OVCD: Bridging Training-Free Foundation Models and Pseudo-Label Learning for Open-Vocabulary Change Detection
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中文总结 AI 辅助
本文提出Zero-OVCD框架，结合无训练基础模型推理与感知噪声的伪标签学习，在多个遥感变化检测数据集上显著提升了开放词汇变化检测性能，且无需目标域像素级标注。
中文摘要 AI 辅助
开放词汇变化检测（Open-Vocabulary Change Detection, OVCD）可识别双时相遥感图像中用户指定的土地覆盖变化，但现有无训练管道仍易受候选掩码不准确、语义分配模糊及推理误差累积的影响。为解决这些问题，本文提出Zero-OVCD，这是一个无需目标域像素级标注的两阶段框架。第一阶段通过互补候选掩码细化、带边际可靠性过滤的多尺度语义相似性融合，以及响应引导的掩码校正与补全，生成高质量变化伪标签；这些组件共同抑制噪声候选、增强掩码级语义区分度并恢复遗漏的变化区域。第二阶段利用生成的伪标签训练变化检测器，同时引入检查点投票与高一致性样本选择以缓解残留伪标签噪声。在LEVIR-CD、WHU-CD和S2Looking数据集上，第一阶段的F1分数分别为86.25%、85.82%和50.48%，第二阶段将其进一步提升至88.65%、88.85%和57.96%；在SECOND数据集的6类一对多任务上，宏平均F1从47.91%提升至50.92%。这些结果表明，将无训练基础模型推理与感知噪声的伪标签学习相结合，可为无需目标域像素级标注的开放词汇变化检测提供有效解决方案。代码将发布在此https URL。
英文摘要
Open-vocabulary change detection (OVCD) enables the identification of user-specified land-cover changes in bitemporal remote sensing images, but existing training-free pipelines remain vulnerable to inaccurate candidate masks, ambiguous semantic assignments, and accumulated inference errors. To address these issues, we propose Zero-OVCD, a two-stage framework that requires no pixel-level annotations from the target domain. In the first stage, high-quality change pseudo-labels are generated through complementary candidate-mask refinement, multiscale semantic similarity fusion with margin-based reliability filtering, and response-guided mask correction and completion. These components jointly suppress noisy candidates, enhance mask-level semantic discrimination, and recover missed change regions. In the second stage, a change detector is trained using the generated pseudo-labels, while checkpoint voting and high-agreement sample selection are introduced to mitigate residual pseudo-label noise. On LEVIR-CD, WHU-CD, and S2Looking, Stage I achieves F1 scores of 86.25%, 85.82%, and 50.48%, while Stage II further improves them to 88.65%, 88.85%, and 57.96%, respectively. On SECOND, the macro-average F1 across six category-wise one-vs-rest tasks increases from 47.91% to 50.92%. These results demonstrate that bridging training-free foundation-model inference with noise-aware pseudo-label learning provides an effective solution for open-vocabulary change detection without target-domain pixel-level annotations. Code will be available at https://github.com/1321663019/Zero-OVCD.
发表机构
- School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology（南京信息工程大学遥感与测绘工程学院）
机构由 AI 辅助整理，请以论文原文为准。
