Current remote sensing change detection (CD) methods mainly rely on specialized models, which limits the scalability toward modality-adaptive Earth observation. For homogeneous CD, precise boundary delineation relies on fine-grained spatial cues and local pixel interactions, whereas heterogeneous CD instead requires broader contextual information to suppress speckle noise and geometric distortions. Moreover, difference operator (e.g., subtraction) works well for aligned homogeneous images but introduces artifacts in cross-modal or geometrically misaligned scenarios. Across different modality settings, specialized models based on static backbones or fixed difference operations often prove insufficient. To address this challenge, we propose UniRoute, a unified framework for modality-adaptive learning by reformulating feature extraction and fusion as conditional routing problems. We introduce an Adaptive Receptive Field Routing MoE (AR2-MoE) module to disentangle local spatial details from global semantic context, and a Modality-Aware Difference Routing MoE (MDR-MoE) module to adaptively select the most suitable fusion primitive at each pixel. In addition, we propose a Consistency-Aware Self-Distillation (CASD) strategy that stabilizes unified training under data-scarce heterogeneous settings by enforcing multi-level consistency. Extensive experiments on five public datasets demonstrate that UniRoute achieves strong overall performance, with a favorable accuracy-efficiency trade-off under a unified deployment setting.

翻译：当前遥感变化检测方法主要依赖专用模型，这限制了其面向模态自适应地球观测的可扩展性。对于同质变化检测，精确的边界划定依赖于细粒度空间线索和局部像素交互；而异质变化检测则需要更广泛的上下文信息以抑制散斑噪声和几何畸变。此外，差分算子（如减法）在配准良好的同质图像中表现优异，但在跨模态或几何未配准场景中会引入伪影。在不同模态设置下，基于静态主干网络或固定差分操作的专用模型往往表现不足。为应对这一挑战，我们提出UniRoute——一个通过将特征提取与融合重新表述为条件路由问题来实现模态自适应学习的统一框架。我们引入了自适应感受野路由混合专家模块，以解耦局部空间细节与全局语义上下文；以及模态感知差分路由混合专家模块，以自适应地为每个像素选择最合适的融合基元。此外，我们提出一致性感知自蒸馏策略，通过强制多层级一致性，在数据稀缺的异质设置下稳定统一训练过程。在五个公开数据集上的大量实验表明，UniRoute在统一部署设置下实现了优异的整体性能，并在精度与效率间取得了良好平衡。