Exchange Is All You Need for Remote Sensing Change Detection

2026-01-12

AI Summary

This work addresses the inefficiency of bitemporal feature fusion and the reliance on explicit differencing in remote sensing change detection by proposing the SEED framework. SEED formalizes a parameter-free feature exchange mechanism as an orthogonal permutation operator, enabling efficient information fusion within a shared-weight Siamese encoder-decoder architecture. Theoretical analysis demonstrates that this mechanism preserves mutual information and approximates the Bayes-optimal risk while maintaining pixel-wise consistency. Notably, any general-purpose segmentation model can be readily converted into a high-performance change detector—termed SEG2CD—by simply embedding the proposed exchange module. Extensive experiments show that SEED achieves state-of-the-art or comparable performance across five benchmarks, including SYSU-CD and LEVIR-CD, confirming its effectiveness and broad applicability.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Feature Construction/Reformulation
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Content-based information diffusion
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• remote sensing change detection
• bi-temporal feature fusion
• feature exchange
• Siamese networks
• information fusion

Innovation

• feature exchange
• parameter-free fusion
• Siamese architecture
• change detection
• mutual information preservation