EfficientViM-CD: An Efficient Remote Sensing Change Detection Network Based on Hidden-State-Mixer

High-resolution optical remote sensing change detection (CD) is of great significance in urban evolution monitoring, disaster assessment, and land management. Traditional deep models often face computational, memory, and inference latency bottlenecks when processing large high-resolution imagery. To address this, we propose EfficientViM-CD: a hidden-state mixer based efficient remote sensing CD network. The approach builds upon the EfficientViM backbone, migrating global interaction operations into a compact hidden state space and leveraging Hidden State Mixer based on state space duality to fuse global context while reducing computational complexity. We employ a Siamese encoding architecture to extract multiscale features and hidden states from paired temporal images, and utilize a cross-hidden fusion module to integrate hidden semantic interactions between time points. At each scale, local difference features are computed and enhanced in hidden state space, and a multiscale decoder reconstructs a pixel-level change probability map. We conducted experiments on four public datasets (LEVIR-CD, WHU-CD, S2Looking, SVCD) and compared against nine state-of-the-art methods. Results demonstrate that EfficientViM-CD achieves competitive accuracy while delivering significant advantages in inference speed and memory efficiency. This method offers a lightweight, efficient, and scalable solution for high-resolution remote sensing CD, with potential for real-time monitoring and emergency response systems.