Abstract
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 this https URL.
Abstract (translated)
遥感变化检测主要依赖于双时相特征的有效融合和区分。现有的方法通常使用通过明确的差分计算模块（如减法或串联）连接的暹罗编码器来识别变化。在本文中，我们提出了一种名为SEED (Siamese Encoder-Exchange-Decoder) 的简化范式，该范式用无参数特征交换替代了这种复杂的差分操作，并通过共享两个暹罗编码器和解码器之间的权重，使其作为一个单一的参数集模型进行有效运作。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的前提下，这一机制能保持互信息和贝叶斯最优风险，而常见的算术融合方法往往会引入信息损失。 通过五个基准测试（包括SYSU-CD、LEVIR-CD、PX-CLCD、WaterCD以及CDD）和三种骨干网络模型（SwinT、EfficientNet 和 ResNet），广泛的实验表明，SEED 方法在简单性的同时能够与现有最佳方法相匹配或超越。此外，我们揭示了标准的语义分割模型仅通过插入这一交换机制即可被转换为竞争力强的变化检测器，这种方法被称为SEG2CD。 所提出的范式提供了一种稳健、统一且可解释性强的框架以实现变化检测，并表明简单的特征交换足以达到高水平的信息融合性能。代码和完整的训练与评估协议将在[此处](https://example.com)发布（注：实际链接请参考原文）。
URL
https://arxiv.org/abs/2601.07805
