Exchange Is All You Need for Remote Sensing Change Detection

TL;DR

This paper introduces SEED, a simple yet effective change detection framework using parameter-free feature exchange, which matches or exceeds state-of-the-art methods across multiple benchmarks and backbones.

Contribution

The paper proposes SEED, a novel change detection paradigm that replaces explicit differencing with parameter-free feature exchange and demonstrates its effectiveness and theoretical soundness.

Findings

SEED matches or surpasses state-of-the-art performance on five benchmarks.

Feature exchange preserves mutual information and Bayes optimal risk under pixel consistency.

Standard semantic segmentation models can be adapted into change detectors using the exchange mechanism.

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 https://github.com/dyzy41/open-rscd.