Abstract
Existing paradigms for remote sensing change detection are caught in a trade-off: CNNs excel at efficiency but lack global context, while Transformers capture long-range dependencies at a prohibitive computational cost. This paper introduces ChangeRWKV, a new architecture that reconciles this conflict. By building upon the Receptance Weighted Key Value (RWKV) framework, our ChangeRWKV uniquely combines the parallelizable training of Transformers with the linear-time inference of RNNs. Our approach core features two key innovations: a hierarchical RWKV encoder that builds multi-resolution feature representation, and a novel Spatial-Temporal Fusion Module (STFM) engineered to resolve spatial misalignments across scales while distilling fine-grained temporal discrepancies. ChangeRWKV not only achieves state-of-the-art performance on the LEVIR-CD benchmark, with an 85.46% IoU and 92.16% F1 score, but does so while drastically reducing parameters and FLOPs compared to previous leading methods. This work demonstrates a new, efficient, and powerful paradigm for operational-scale change detection. Our code and model are publicly available.
Abstract (translated)
现有遥感变化检测范式陷入效率与全局信息的权衡困境：CNN效率高但缺乏全局上下文，而Transformer虽能捕捉长程依赖却计算成本高昂。本研究提出ChangeRWKV新架构以化解此矛盾，其基于Receptance Weighted Key Value（RWKV）框架，创新性地融合了Transformer的可并行训练特性与RNN的线性时间复杂度推理能力。该方法核心包含两项关键创新：一是构建多分辨率特征表征的层级化RWKV编码器；二是专为消除跨尺度空间错位并提炼细粒度时序差异而设计的新型时空融合模块（STFM）。ChangeRWKV在LEVIR-CD基准测试中取得85.46%交并比与92.16% F1分数的state-of-the-art性能，同时相较于此前领先方法大幅缩减了参数量与计算量。本研究展示了一种适用于实际应用场景的高效强大变化检测新范式，代码与模型已公开。
URL
https://arxiv.org/abs/2603.19606
