Unsupervised change detection (UCD) in remote sensing aims to localise semantic changes between two images of the same region without relying on labelled data during training. Most recent approaches rely either on frozen foundation models in a training-free manner or on training with synthetic changes generated in pixel space. Both strategies inherently rely on predefined assumptions about change types, typically introduced through handcrafted rules, external datasets, or auxiliary generative models. Due to these assumptions, such methods fail to generalise beyond a few change types, limiting their real-world usage, especially in rare or complex scenarios. To address this, we propose MaSoN (Make Some Noise), an end-to-end UCD framework that synthesises diverse changes directly in the latent feature space during training. It generates changes that are dynamically estimated using feature statistics of target data, enabling diverse yet data-driven variation aligned with the target domain. It also easily extends to new modalities, such as SAR. MaSoN generalises strongly across diverse change types and achieves state-of-the-art performance on five benchmarks, improving the average F1 score by 14.1 percentage points. Project page: https://blaz-r.github.io/mason_ucd

翻译：无监督遥感变化检测旨在无需依赖训练期间的标注数据，定位同一区域两幅图像之间的语义变化。当前主流方法主要依赖两种策略：一是以免训练方式使用冻结的基础模型，二是利用在像素空间中生成的合成变化进行训练。这两种策略本质上都依赖于对变化类型的预定义假设，这些假设通常通过手工规则、外部数据集或辅助生成模型引入。由于这些假设的存在，此类方法难以泛化到少数几种变化类型之外，限制了其在真实场景（尤其是罕见或复杂场景）中的应用。为解决这一问题，我们提出了MaSoN（Make Some Noise）——一种端到端的无监督变化检测框架，该框架在训练期间直接在潜在特征空间中合成多样化的变化。它通过利用目标数据的特征统计量进行动态估计来生成变化，从而产生多样化且与目标领域对齐的数据驱动变化。该方法还能轻松扩展到新模态（如合成孔径雷达）。MaSoN在多种变化类型上展现出强大的泛化能力，并在五个基准测试中取得了最先进的性能，将平均F1分数提升了14.1个百分点。项目页面：https://blaz-r.github.io/mason_ucd