Change detection (CD) aims to identify surface changes from multi-temporal remote sensing imagery. In real-world scenarios, Pixel-level change labels are expensive to acquire, and existing models struggle to adapt to scenarios with diverse annotation availability. To tackle this challenge, we propose a unified change detection framework (UniCD), which collaboratively handles supervised, weakly-supervised, and unsupervised tasks through a coupled architecture. UniCD eliminates architectural barriers through a shared encoder and multi-branch collaborative learning mechanism, achieving deep coupling of heterogeneous supervision signals. Specifically, UniCD consists of three supervision-specific branches. In the supervision branch, UniCD introduces the spatial-temporal awareness module (STAM), achieving efficient synergistic fusion of bi-temporal features. In the weakly-supervised branch, we construct change representation regularization (CRR), which steers model convergence from coarse-grained activations toward coherent and separable change modeling. In the unsupervised branch, we propose semantic prior-driven change inference (SPCI), which transforms unsupervised tasks into controlled weakly-supervised path optimization. Experiments on mainstream datasets demonstrate that UniCD achieves optimal performance across three tasks. It exhibits significant accuracy improvements in weakly and unsupervised scenarios, surpassing current state-of-the-art by 12.72% and 12.37% on LEVIR-CD, respectively.

翻译：变化检测（CD）旨在从多时相遥感影像中识别地表变化。在实际场景中，像素级变化标签获取成本高昂，且现有模型难以适应标注可用性多样化的场景。为应对这一挑战，我们提出了一种统一变化检测框架（UniCD），其通过耦合架构协同处理全监督、弱监督和无监督任务。UniCD通过共享编码器与多分支协同学习机制消除了架构壁垒，实现了异构监督信号的深度耦合。具体而言，UniCD包含三个针对特定监督方式的分支。在全监督分支中，UniCD引入了时空感知模块（STAM），实现了双时相特征的高效协同融合。在弱监督分支中，我们构建了变化表示正则化（CRR），引导模型从粗粒度激活向连贯且可分离的变化建模方向收敛。在无监督分支中，我们提出了语义先验驱动的变化推理（SPCI），将无监督任务转化为受控的弱监督路径优化问题。在主流数据集上的实验表明，UniCD在三种任务上均取得了最优性能。其在弱监督和无监督场景下表现出显著的精度提升，在LEVIR-CD数据集上分别超越了当前最优方法12.72%和12.37%。