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.
Zhao et al. (Tue,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: