用RWKV架构实现高效精准的遥感变化检测。
Beyond Quadratic: Linear-Time Change Detection with RWKV
- 基于RWKV框架，结合Transformer与RNN优势。
- 在LEVIR-CD上达85.46% IoU和92.16% F1，参数与算力大幅降低。
- 适合需要实时处理的大规模遥感变化监测任务。
现有遥感变化检测方法面临效率与全局上下文的权衡：CNN虽高效但缺乏长程依赖，而Transformer虽能捕捉远距离关系却计算成本过高。本文提出ChangeRWKV，基于受体加权键值（RWKV）框架，将Transformer的可并行训练与RNN的线性推理时间相结合。核心创新包括：分层RWKV编码器构建多分辨率特征表示，以及专为解决跨尺度空间错位并提炼细粒度时序差异设计的时空融合模块（STFM）。ChangeRWKV在LEVIR-CD基准上达到85.46% IoU和92.16% F1，同时相比先前领先方法显著减少参数量与浮点运算量。本工作展示了一种面向业务化应用的高效且强大的变化检测新范式。代码与模型已公开。
原文摘要 · Abstract (English)
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.
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