Change-Aware Remote Sensing Spatiotemporal Fusion via Frequency Decoupling and Symmetric Gating

Abstract

Remote sensing spatiotemporal fusion (STF) remains a formidable challenge in scenarios with complex land-cover changes. Existing methods generally use masking mechanisms to cover changed regions, aiming to mitigate alignment biases induced by change information. However, such indiscriminate masking strategies ignore transferable structural cues embedded in pseudo changes, leading to texture collapse and boundary degradation. To address these issues, we propose a change-aware (CA) remote sensing STF method via frequency decoupling and symmetric gating, termed CA-STF. Specifically, a wavelet feature modulation module is first designed to perform frequency decoupling, which extracts reusable structural cues from pseudo changes under low-frequency guidance while suppressing spectral perturbations. Subsequently, a spatiotemporal synergistic enhancement module employs a symmetric gating mechanism to selectively fuse the cue-enhanced features, adaptively decoupling changed and unchanged regions. Finally, a mixture-of-experts-based spatial restoration module decodes the gated features to reconstruct fine-grained textures in changed areas. Experimental results on the public the Lower Gwydir Catchment and Coleambally irrigation area datasets demonstrate that our CA-STF achieves superior fusion accuracy and visual quality in complex spatiotemporal dynamics.