Remote sensing change detection fundamentally relies on the effective fusion and discrimination of bi-temporal features. Prevailing paradigms typically utilize Siamese encoders bridged by explicit difference computation modules, such as subtraction or concatenation, to identify changes. In this work, we challenge this complexity with SEED (Siamese Encoder-Exchange-Decoder), a streamlined paradigm that replaces explicit differencing with parameter-free feature exchange. By sharing weights across both Siamese encoders and decoders, SEED effectively operates as a single parameter set model. Theoretically, we formalize feature exchange as an orthogonal permutation operator and prove that, under pixel consistency, this mechanism preserves mutual information and Bayes optimal risk, whereas common arithmetic fusion methods often introduce information loss. Extensive experiments across five benchmarks, including SYSU-CD, LEVIR-CD, PX-CLCD, WaterCD, and CDD, and three backbones, namely SwinT, EfficientNet, and ResNet, demonstrate that SEED matches or surpasses state of the art methods despite its simplicity. Furthermore, we reveal that standard semantic segmentation models can be transformed into competitive change detectors solely by inserting this exchange mechanism, referred to as SEG2CD. The proposed paradigm offers a robust, unified, and interpretable framework for change detection, demonstrating that simple feature exchange is sufficient for high performance information fusion. Code and full training and evaluation protocols will be released at https://github.com/dyzy41/open-rscd.

翻译：遥感变化检测本质上依赖于双时相特征的有效融合与区分。主流范式通常采用孪生编码器架构，并通过显式的差异计算模块（如减法或拼接）进行桥接以识别变化。在本工作中，我们提出了一种简洁的范式SEED（孪生编码器-交换-解码器），以无参数的特征交换取代显式的差异计算，从而挑战这种复杂性。通过在孪生编码器和解码器之间共享权重，SEED实际上作为一个单一参数集模型运行。理论上，我们将特征交换形式化为一个正交置换算子，并证明在像素一致性条件下，该机制能够保持互信息和贝叶斯最优风险，而常见的算术融合方法则常常引入信息损失。在五个基准数据集（包括SYSU-CD、LEVIR-CD、PX-CLCD、WaterCD和CDD）和三种骨干网络（即SwinT、EfficientNet和ResNet）上进行的大量实验表明，尽管结构简洁，SEED的性能与当前最先进方法相当或更优。此外，我们发现标准的语义分割模型仅需插入这种交换机制（称为SEG2CD），即可转化为具有竞争力的变化检测器。所提出的范式为变化检测提供了一个鲁棒、统一且可解释的框架，证明了简单的特征交换足以实现高性能的信息融合。代码及完整的训练与评估协议将在 https://github.com/dyzy41/open-rscd 发布。