FACDNet: A Frequency-Aware Cross-Layer Network for Remote Sensing Change Detection

Remote sensing change detection is crucial for urban expansion monitoring and ecological assessment. Recently, methods based on Convolutional Neural Networks (CNNs) and Transformers have advanced significantly. However, state-of-the-art models relying primarily on pure spatial-domain modeling and absolute feature differences struggle to balance global semantics with high-frequency boundary details. This paradigm loses physical change directionality and amplifies pseudo-change noise in complex backgrounds. To overcome this, we propose a Frequency-Aware Cross-Layer Change Detection Network (FACDNet) that leverages frequency-spatial synergy to enhance feature discriminability. Specifically, a Wavelet Interaction Block (WIB) decouples bitemporal features using Haar wavelets, employing heterogeneous attention to targetedly reinforce macroscopic semantics and edge textures. Furthermore, to mitigate noise in shallow features, a Cross-Layer Frequency Context Aggregator (CLFCA) injects deep global semantics top-down, purifying multi-scale spatial gating signals. Finally, a Context-guided Difference Fusion Module (CDFM) extracts direction-aware bidirectional difference features, utilizing the purified gating to accurately suppress pseudo-changes. Extensive experiments on the LEVIR-CD and highly challenging SHCD datasets demonstrate FACDNet’s remarkable robustness. It achieves change-class F1-scores of 92.04% and 83.64%, and Intersection over Union (IoU) scores of 85.26% and 71.89%, respectively, achieving highly competitive performance compared with existing mainstream methods.