High-Resolution Remote Sensing Image Change Detection Based on Fourier Feature Interaction and Multi-scale Perception

As a significant means of Earth observation, change detection in high-resolution remote sensing images has received extensive attention. Nevertheless, the variability in imaging conditions introduces style discrepancies and a range of pseudo change regions between bi-temporal image pairs. Furthermore, changing objects possess diverse morphological representations, which makes accurately identifying change areas and delineating their boundaries within complex object distributions increasingly difficult. In response to the aforementioned challenges, we propose Fourier feature interaction and multi-scale perception (FIMP) model for effective change detection. To mitigate the impact of style discrepancies, FIMP employs the Fourier transform to adaptively filter bi-temporal features in the frequency domain, whilst mining the optimized bi-temporal features relevant to the change detection task. To enhance the ability to recognize multi-scale changing objects, FIMP aggregates and emphasizes the change areas with the introduced temporal change enhancement module (TCEM). By utilizing the U-fusion change perception module (UCPM) to perform multi-level bidirectional fusion of change features at different scales, FIMP can further enhance the ability to delineate complex semantic change boundaries. Experiments on three public datasets shows that our approach outperforms seven state-of-the-art methods.