Unsupervised building change detection aims to learn building-change masks from unlabeled bi-temporal remote sensing images. Existing label-free methods often follow a discrepancy-to-mask paradigm, directly using temporal differences, frozen foundation-model responses, prompt-based outputs, or post-processing results as final change maps. Although these strategies provide annotation-free cues, they do not learn a task-specific building-change detector and remain vulnerable to the gap between generic temporal discrepancies and building-defined structural changes. In practice, such discrepancies are often noisy and task-irrelevant, as appearance shifts, registration errors, and non-building modifications can produce strong but misleading responses. To address this problem, we propose SST-CD, a spatially selective self-training framework that reformulates fully label-free building change detection as end-to-end detector learning under noisy pseudo supervision. SST-CD uses temporal discrepancies as candidate pseudo labels and trains the detector only on spatially reliable pixels, whose reliability is estimated by a local consistency criterion that filters inconsistent regions from supervision. To further stabilize noisy self-training, a lightweight feature adapter recalibrates bi-temporal features, while a prototype-based decoder produces compact change and no-change representations. Experiments on LEVIR-CD, WHU-CD, and DSIFN-CD show that SST-CD achieves F1 scores of 83.08%, 91.69%, and 86.60%, respectively, outperforming existing unsupervised and label-free baselines.

翻译：无监督建筑物变化检测旨在从未标记的双时相遥感图像中学习建筑物变化掩膜。现有无标签方法通常遵循差异到掩膜范式，直接使用时相差异、冻结基础模型响应、基于提示的输出或后处理结果作为最终变化图。虽然这些策略提供了无标注线索，但它们并未学习任务特定的建筑物变化检测器，且仍易受通用时相差异与建筑物定义的结构变化之间的差距影响。实践中，此类差异往往带有噪声且与任务无关，因为外观偏移、配准误差及非建筑物修改可能产生强但误导性的响应。为解决此问题，我们提出SST-CD，一种空间选择性自训练框架，将完全无标签的建筑物变化检测重新表述为带噪声伪监督下的端到端检测器学习。SST-CD将时相差异用作候选伪标签，并仅对空间可靠像素训练检测器，其可靠性通过局部一致性准则估计，该准则从监督中过滤不一致区域。为进一步稳定有噪声的自训练，轻量级特征适配器重新校准双时相特征，而基于原型的解码器生成紧凑的变化与无变化表示。在LEVIR-CD、WHU-CD和DSIFN-CD上的实验表明，SST-CD分别达到83.08%、91.69%和86.60%的F1分数，优于现有无监督和无标签基线方法。